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Donald Stephen Poskitt

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. David T. Frazier & Ryan Covey & Gael M. Martin & Donald Poskitt, 2023. "Solving the Forecast Combination Puzzle," Papers 2308.05263, arXiv.org.

    Cited by:

    1. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).

  2. Ryan Zischke & Gael M. Martin & David T. Frazier & D. S. Poskitt, 2022. "The Impact of Sampling Variability on Estimated Combinations of Distributional Forecasts," Papers 2206.02376, arXiv.org.

    Cited by:

    1. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.

  3. Chuhui Li & Donald S Poskitt & Frank Windmeijer & Xueyan Zhao, 2019. "Binary Outcomes, OLS, 2SLS and IV Probit," Monash Econometrics and Business Statistics Working Papers 5/19, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Jutao Zeng & Jie Lyu, 2023. "Simultaneous Decisions to Undertake Off-Farm Work and Straw Return: The Role of Cognitive Ability," Land, MDPI, vol. 12(8), pages 1-21, August.
    2. Yang Yang, 2023. "Hukou Identity and Economic Behaviours: A Social Identity Perspective," Erudite Ph.D Dissertations, Erudite, number ph23-02 edited by Catherine Bros & Julie Lochard.
    3. Dominik Wied, 2022. "Semiparametric Distribution Regression with Instruments and Monotonicity," Papers 2212.03704, arXiv.org.
    4. Dakyung Seong, 2022. "Binary response model with many weak instruments," Papers 2201.04811, arXiv.org, revised Jun 2024.

  4. Chuhui Li & Donald S. Poskitt & Xueyan Zhao, 2016. "The Bivariate Probit Model, Maximum Likelihood Estimation, Pseudo True Parameters and Partial Identification," Monash Econometrics and Business Statistics Working Papers 16/16, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Liqiong Lin & Weizhuo Wang & Christopher Gan & David A. Cohen & Quang T.T Nguyen, 2019. "Rural Credit Constraint and Informal Rural Credit Accessibility in China," Sustainability, MDPI, vol. 11(7), pages 1-20, April.
    2. Esther Hauk & Monica Oviedo & Xavier Ramos, 2017. "Perception of Corruption and Public Support for Redistribution in Latin America," Working Papers 974, Barcelona School of Economics.
    3. Santiago Acerenza & Otávio Bartalotti & Désiré Kédagni, 2023. "Testing identifying assumptions in bivariate probit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 407-422, April.
    4. Abayomi Samuel Oyekale, 2021. "Willingness to Take COVID-19 Vaccines in Ethiopia: An Instrumental Variable Probit Approach," IJERPH, MDPI, vol. 18(17), pages 1-11, August.
    5. Qinan Lu & Xiaodong Du & Huanguang Qiu, 2022. "Adoption patterns and productivity impacts of agricultural mechanization services," Agricultural Economics, International Association of Agricultural Economists, vol. 53(5), pages 826-845, September.
    6. Hasanov, Rashad & Bhattacharya, Prasad Sankar, 2019. "Do political factors influence banking crisis?," Economic Modelling, Elsevier, vol. 76(C), pages 305-318.
    7. Ponguane, Sérgio & Mucavele, Nézia, 2018. "Determinants of Agricultural Technology Adoption in Chókwè District, Mozambique," MPRA Paper 86284, University Library of Munich, Germany, revised 13 Apr 2018.
    8. David T. Frazier & Eric Renault & Lina Zhang & Xueyan Zhao, 2020. "Weak Identification in Discrete Choice Models," Papers 2011.06753, arXiv.org, revised Jan 2021.
    9. Lina Zhang & David T. Frazier & D. S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Papers 2009.02642, arXiv.org, revised Sep 2022.
    10. Di Novi, Cinzia & Martini, Gianmaria & Sturaro, Caterina, 2023. "The impact of informal and formal care disruption on older adults’ psychological distress during the COVID-19 pandemic in UK," Economics & Human Biology, Elsevier, vol. 49(C).
    11. Donald S. Poskitt & Xueyan Zhao, 2023. "Bootstrap Hausdorff Confidence Regions for Average Treatment Effect Identified Sets," Monash Econometrics and Business Statistics Working Papers 9/23, Monash University, Department of Econometrics and Business Statistics.
    12. Li, Chen & Swaminathan, Srinivasan & Kim, Junhee, 2021. "The role of marketing channels in consumers’ promotional point redemption decisions," Journal of Business Research, Elsevier, vol. 125(C), pages 314-323.
    13. Jing Peng, 2023. "Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis," Information Systems Research, INFORMS, vol. 34(1), pages 67-84, March.
    14. Juan Diaz & Nicolas Grau & Tatiana Reyes & Jorge Rivera, 2021. "The Impact of Grade Retention on Juvenile Crime," Working Papers wp513, University of Chile, Department of Economics.
    15. Genc Zhushi & Driton Qehaja, 2024. "Triadic relationship of remittances, migration and labor force," International Journal of Development Issues, Emerald Group Publishing Limited, vol. 23(3), pages 463-488, May.
    16. Arora, Varun & Chakravarty, Sujoy & Kapoor, Hansika & Mukherjee, Shagata & Roy, Shubhabrata & Tagat, Anirudh, 2023. "No going back: COVID-19 disease threat perception and male migrants' willingness to return to work in India," Journal of Economic Behavior & Organization, Elsevier, vol. 209(C), pages 533-546.
    17. Edobor, Edeoba W. & Wiatt, Renee D. & Marshall, Maria I., 2021. "Keeping the farm business in the family: the case of farm and non-farm family businesses in the midwestern United States," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(6), February.
    18. William Greene & Mark N. Harris & Preety Srivastava & Xueyan Zhao, 2018. "Misreporting and econometric modelling of zeros in survey data on social bads: An application to cannabis consumption," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 372-389, February.
    19. Lungu, Harad Chuma, 2019. "Determinants of climate smart agricultural technology adoption in the Northern Province of Zambia," Research Theses 334754, Collaborative Masters Program in Agricultural and Applied Economics.
    20. Li, Chuhui & Cheng, Wenli & Shi, Hui, 2021. "Early marriage and maternal health care utilisation: Evidence from sub-Saharan Africa," Economics & Human Biology, Elsevier, vol. 43(C).
    21. Marther W. Ngigi & Elijah N. Muange, 2022. "Access to climate information services and climate-smart agriculture in Kenya: a gender-based analysis," Climatic Change, Springer, vol. 174(3), pages 1-23, October.
    22. Di Novi, Cinzia & Kovacic, Matija & Orso, Cristina Elisa, 2024. "Online health information seeking behavior, healthcare access, and health status during exceptional times," Journal of Economic Behavior & Organization, Elsevier, vol. 220(C), pages 675-690.
    23. Craig E. Landry & Dylan Turner & Daniel Petrolia, 2021. "Flood Insurance Market Penetration and Expectations of Disaster Assistance," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 79(2), pages 357-386, June.
    24. Wang, Menghan & Liu, Zhong & Xu, Aiyan & Yang, Dan, 2022. "Fuel choice for rural Tibetan households: Impacts of access to credit," Energy Economics, Elsevier, vol. 115(C).
    25. Amadu, Festus O. & McNamara, Paul E. & Miller, Daniel C., 2020. "Understanding the adoption of climate-smart agriculture: A farm-level typology with empirical evidence from southern Malawi," World Development, Elsevier, vol. 126(C).
    26. Rong Xu & Yating Zhan & Jialan Zhang & Qiang He & Kuan Zhang & Dingde Xu & Yanbin Qi & Xin Deng, 2022. "Does Construction of High-Standard Farmland Improve Recycle Behavior of Agricultural Film? Evidence from Sichuan, China," Agriculture, MDPI, vol. 12(10), pages 1-14, October.
    27. Brenna, Elenka & Giammanco, Maria Daniela, 2024. "The use of voluntary health insurance in the access to specialist care: Evidence from the Italian NHS," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    28. Yili Hong & Jing Peng & Gordon Burtch & Ni Huang, 2021. "Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets," Information Systems Research, INFORMS, vol. 32(3), pages 786-800, September.

  5. George Athanasopoulos & D.S. Poskitt & Farshid Vahid & Wenying Yao, 2014. "Determination of long-run and short-run dynamics in EC-VARMA models via canonical correlations," Monash Econometrics and Business Statistics Working Papers 22/14, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Mengheng Li & Ivan Mendieta-Munoz, 2019. "The multivariate simultaneous unobserved components model and identification via heteroskedasticity," Working Paper Series 2019/08, Economics Discipline Group, UTS Business School, University of Technology, Sydney.

  6. K. Nadarajah & Gael M. Martin & D.S. Poskitt, 2014. "Issues in the Estimation of Mis-Specified Models of Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 18/14, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.

  7. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    2. Eickmeier, Sandra & Ng, Tim, 2011. "How Do Credit Supply Shocks Propagate Internationally? A GVAR approach," CEPR Discussion Papers 8720, C.E.P.R. Discussion Papers.
    3. Joshua C C Chan & Eric Eisenstat & Gary Koop, 2014. "Large Bayesian VARMAs," Working Papers 1409, University of Strathclyde Business School, Department of Economics.
    4. Varang Wiriyawit & Benjamin Wong, 2014. "Structural VARs, Deterministic and Stochastic Trends: Does Detrending Matter?," CAMA Working Papers 2014-46, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

  8. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    2. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2013. "Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 25/13, Monash University, Department of Econometrics and Business Statistics.

  9. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2012. "Higher Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 9/12, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    2. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.
    3. La Vecchia, Davide & Ronchetti, Elvezio, 2019. "Saddlepoint approximations for short and long memory time series: A frequency domain approach," Journal of Econometrics, Elsevier, vol. 213(2), pages 578-592.
    4. Masoud M. Nasari & Mohamedou Ould-Haye, 2022. "Confidence intervals with higher accuracy for short and long-memory linear processes," Statistical Papers, Springer, vol. 63(4), pages 1187-1220, August.
    5. Arteche González, Jesús María, 2020. "Frequency Domain Local Bootstrap in long memory time series," BILTOKI info:eu-repo/grantAgreeme, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    6. Arteche, Josu, 2024. "Bootstrapping long memory time series: Application in low frequency estimators," Econometrics and Statistics, Elsevier, vol. 29(C), pages 1-15.

  10. Md Atikur Rahman Khan & D.S. Poskitt, 2011. "Window Length Selection and Signal-Noise Separation and Reconstruction in Singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 23/11, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Franz Ruch & Dirk Bester, 2013. "Towards a Measure of Core Inflation using Singular Spectrum Analysis," South African Journal of Economics, Economic Society of South Africa, vol. 81(3), pages 307-329, September.
    2. Hossein Hassani & Zara Ghodsi & Rangan Gupta & Mawuli K. Segnon, 2014. "Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis," Working Papers 201482, University of Pretoria, Department of Economics.

  11. Md Atikur Rahman Khan & D.S. Poskitt, 2011. "Moment Tests for Window Length Selection in Singular Spectrum Analysis of Short- and Long-Memory Processes," Monash Econometrics and Business Statistics Working Papers 22/11, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    2. Andrés Berenguer & Luis Gandarias & Álvaro Arévalo, 2020. "Singular spectrum analysis for modelling the hard-to-model risk factors," Risk Management, Palgrave Macmillan, vol. 22(3), pages 178-191, September.
    3. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.

  12. Md Atikur Rahman Khan & D.S. Poskitt, 2010. "Description Length Based Signal Detection in singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 13/10, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Md Atikur Rahman Khan & D.S. Poskitt, 2011. "Moment Tests for Window Length Selection in Singular Spectrum Analysis of Short- and Long-Memory Processes," Monash Econometrics and Business Statistics Working Papers 22/11, Monash University, Department of Econometrics and Business Statistics.
    2. Md Atikur Rahman Khan & D.S. Poskitt, 2011. "Window Length Selection and Signal-Noise Separation and Reconstruction in Singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 23/11, Monash University, Department of Econometrics and Business Statistics.
    3. M. Atikur Rahman Khan & D.S. Poskitt, 2014. "On The Theory and Practice of Singular Spectrum Analysis Forecasting," Monash Econometrics and Business Statistics Working Papers 3/14, Monash University, Department of Econometrics and Business Statistics.
    4. Telesca, Luciano & Laib, Mohamed & Guignard, Fabian & Mauree, Dasaraden & Kanevski, Mikhail, 2019. "Linearity versus non-linearity in high frequency multilevel wind time series measured in urban areas," Chaos, Solitons & Fractals, Elsevier, vol. 120(C), pages 234-244.

  13. Shuowen Hu & D.S. Poskitt & Xibin Zhang, 2010. "Bayesian Adaptive Bandwidth Kernel Density Estimation of Irregular Multivariate Distributions," Monash Econometrics and Business Statistics Working Papers 21/10, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Y. Ziane & S. Adjabi & N. Zougab, 2015. "Adaptive Bayesian bandwidth selection in asymmetric kernel density estimation for nonnegative heavy-tailed data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1645-1658, August.
    2. Shuowen Hu & D.S. Poskitt & Xibin Zhang, 2010. "Bayesian Adaptive Bandwidth Kernel Density Estimation of Irregular Multivariate Distributions," Monash Econometrics and Business Statistics Working Papers 21/10, Monash University, Department of Econometrics and Business Statistics.
    3. Zougab, Nabil & Adjabi, Smail & Kokonendji, Célestin C., 2014. "Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 28-38.
    4. Yasmina Ziane & Nabil Zougab & Smail Adjabi, 2018. "Birnbaum–Saunders power-exponential kernel density estimation and Bayes local bandwidth selection for nonnegative heavy tailed data," Computational Statistics, Springer, vol. 33(1), pages 299-318, March.
    5. Tristan Senga Kiessé & Nabil Zougab & Célestin C. Kokonendji, 2016. "Bayesian estimation of bandwidth in semiparametric kernel estimation of unknown probability mass and regression functions of count data," Computational Statistics, Springer, vol. 31(1), pages 189-206, March.
    6. Ziane Yasmina & Zougab Nabil & Adjabi Smail, 2021. "Body tail adaptive kernel density estimation for nonnegative heavy-tailed data," Monte Carlo Methods and Applications, De Gruyter, vol. 27(1), pages 57-69, March.

  14. D.S. Poskitt, 2009. "Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory," Monash Econometrics and Business Statistics Working Papers 12/09, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Christian Kascha & Carsten Trenkler, 2011. "Cointegrated VARMA models and forecasting US interest rates," ECON - Working Papers 033, Department of Economics - University of Zurich.

  15. D. S. Poskitt & Arivalzahan Sengarapillai, 2009. "Description Length and Dimensionality Reduction in Functional Data Analysis," Monash Econometrics and Business Statistics Working Papers 13/09, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Han Lin Shang, 2011. "A survey of functional principal component analysis," Monash Econometrics and Business Statistics Working Papers 6/11, Monash University, Department of Econometrics and Business Statistics.
    2. Wong, Raymond K.W. & Zhang, Xiaoke, 2019. "Nonparametric operator-regularized covariance function estimation for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 131-144.
    3. Tengteng Xu & Riquan Zhang & Xiuzhen Zhang, 2023. "Estimation of spatial-functional based-line logit model for multivariate longitudinal data," Computational Statistics, Springer, vol. 38(1), pages 79-99, March.
    4. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    5. Md Atikur Rahman Khan & D.S. Poskitt, 2010. "Description Length Based Signal Detection in singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 13/10, Monash University, Department of Econometrics and Business Statistics.

  16. George Athanasopoulos & D.S. Poskitt & Farshid Vahid, 2007. "Two canonical VARMA forms: Scalar component models vis-à-vis the Echelon form," Monash Econometrics and Business Statistics Working Papers 10/07, Monash University, Department of Econometrics and Business Statistics, revised May 2009.

    Cited by:

    1. George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    2. D.S. Poskitt, 2009. "Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory," Monash Econometrics and Business Statistics Working Papers 12/09, Monash University, Department of Econometrics and Business Statistics.
    3. Luis A. Gil-Alana & Rangan Gupta & Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2015. "Time Series Analysis of Persistence in Crude Oil Price Volatility across Bull and Bear Regimes," Working Papers 201580, University of Pretoria, Department of Economics.
    4. Mendoza, Daniel E. & Ochoa-Sánchez, Ana & Samaniego, Esteban P., 2022. "Forecasting of a complex phenomenon using stochastic data-based techniques under non-conventional schemes: The SARS-CoV-2 virus spread case," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    5. Dias, Gustavo Fruet & Kapetanios, George, 2018. "Estimation and forecasting in vector autoregressive moving average models for rich datasets," Journal of Econometrics, Elsevier, vol. 202(1), pages 75-91.
    6. Siva R Venna & Satya Katragadda & Vijay Raghavan & Raju Gottumukkala, 2021. "River Stage Forecasting using Enhanced Partial Correlation Graph," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4111-4126, September.
    7. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Abhimanyu Mukerji & Sushant More & Ashwin Viswanathan Kannan & Lakshmi Ravi & Hua Chen & Naman Kohli & Chris Khawand & Dinesh Mandalapu, 2024. "Valuing an Engagement Surface using a Large Scale Dynamic Causal Model," Papers 2408.11967, arXiv.org.
    9. Athanasopouolos, George & Poskitt, Don & Vahid, Farshid & Yao, Wenying, 2014. "Forecasting with EC-VARMA models," Working Papers 2014-07, University of Tasmania, Tasmanian School of Business and Economics, revised 22 Feb 2014.

  17. S. D. Grose & D. S. Poskitt, 2006. "The Finite-Sample Properties of Autoregressive Approximations of Fractionally-Integrated and Non-Invertible Processes," Monash Econometrics and Business Statistics Working Papers 15/06, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.
    2. D. S. Poskitt, 2006. "Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes," Monash Econometrics and Business Statistics Working Papers 12/06, Monash University, Department of Econometrics and Business Statistics.
    3. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2013. "Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 25/13, Monash University, Department of Econometrics and Business Statistics.

  18. D. S. Poskitt, 2006. "Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes," Monash Econometrics and Business Statistics Working Papers 12/06, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. George Kapetanios & Fotis Papailias, 2011. "Block Bootstrap and Long Memory," Working Papers 679, Queen Mary University of London, School of Economics and Finance.
    2. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    3. Cassola, Nuno & Morana, Claudio, 2010. "Comovements in volatility in the euro money market," Journal of International Money and Finance, Elsevier, vol. 29(3), pages 525-539, April.
    4. Dong Jin Lee, 2021. "Bootstrap tests for structural breaks when the regressors and the serially correlated error term are unstable," Bulletin of Economic Research, Wiley Blackwell, vol. 73(2), pages 212-229, April.
    5. Arteche, Josu & Orbe, Jesus, 2016. "A bootstrap approximation for the distribution of the Local Whittle estimator," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 645-660.
    6. Md Atikur Rahman Khan & D.S. Poskitt, 2011. "Moment Tests for Window Length Selection in Singular Spectrum Analysis of Short- and Long-Memory Processes," Monash Econometrics and Business Statistics Working Papers 22/11, Monash University, Department of Econometrics and Business Statistics.
    7. Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
    8. Zhanshou Chen & Yanting Xiao & Fuxiao Li, 2021. "Monitoring memory parameter change-points in long-memory time series," Empirical Economics, Springer, vol. 60(5), pages 2365-2389, May.
    9. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    10. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.
    11. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    12. Rupasinghe, Maduka & Samaranayake, V.A., 2012. "Asymptotic properties of sieve bootstrap prediction intervals for FARIMA processes," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2108-2114.
    13. Zacharias Psaradakis & Marian Vavra, 2017. "Normality Tests for Dependent Data," Working and Discussion Papers WP 12/2017, Research Department, National Bank of Slovakia.
    14. Margherita Gerolimetto & Stefano Magrini, 2020. "Testing for boundary conditions in case of fractionally integrated processes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 357-371, June.
    15. Marian Vavra, 2018. "Assessing Distributional Properties of Forecast Errors," Working and Discussion Papers WP 3/2018, Research Department, National Bank of Slovakia.
    16. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2013. "Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 25/13, Monash University, Department of Econometrics and Business Statistics.
    17. F. Giordano & M. La Rocca & C. Perna, 2011. "Properties of the neural network sieve bootstrap," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 803-817.
    18. Adam McCloskey, 2012. "Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends," Working Papers 2012-17, Brown University, Department of Economics.
    19. Zacharias Psaradakis & Marián Vávra, 2015. "A Distance Test of Normality for a Wide Class of Stationary Processes," Birkbeck Working Papers in Economics and Finance 1513, Birkbeck, Department of Economics, Mathematics & Statistics.
    20. Claudio Morana, 2007. "On the macroeconomic causes of exchange rates volatility," ICER Working Papers 8-2007, ICER - International Centre for Economic Research.
    21. Masoud M. Nasari & Mohamedou Ould-Haye, 2022. "Confidence intervals with higher accuracy for short and long-memory linear processes," Statistical Papers, Springer, vol. 63(4), pages 1187-1220, August.
    22. Arteche González, Jesús María, 2020. "Frequency Domain Local Bootstrap in long memory time series," BILTOKI info:eu-repo/grantAgreeme, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    23. Arteche, Josu, 2024. "Bootstrapping long memory time series: Application in low frequency estimators," Econometrics and Statistics, Elsevier, vol. 29(C), pages 1-15.

  19. D. S. Poskitt, 2005. "Autoregressive Approximation in Nonstandard Situations: The Non-Invertible and Fractionally Integrated Cases," Monash Econometrics and Business Statistics Working Papers 16/05, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.
    2. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    3. George Kapetanios & Zacharias Psaradakis, 2007. "Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence," Working Papers 587, Queen Mary University of London, School of Economics and Finance.
    4. George Kapetanios & Andrew P. Blake, 2007. "Testing the Martingale Difference Hypothesis Using Neural Network Approximations," Working Papers 601, Queen Mary University of London, School of Economics and Finance.
    5. Richard T. Baillie & George Kapetanios, 2006. "Nonlinear Models with Strongly Dependent Processes and Applications to Forward Premia and Real Exchange Rates," Working Papers 570, Queen Mary University of London, School of Economics and Finance.

  20. D.S. Poskitt & C.L. Skeels, 2005. "Small Concentration Asymptotics and Instrumental Variables Inference," Department of Economics - Working Papers Series 948, The University of Melbourne.

    Cited by:

    1. Russell Davidson & James MacKinnon, 2006. "Bootstrap Inference In A Linear Equation Estimated By Instrumental Variables," Departmental Working Papers 2006-21, McGill University, Department of Economics.
    2. Adrian Pagan, 2007. "Weak instruments (in Russian)," Quantile, Quantile, issue 2, pages 71-81, March.

  21. D. S. Poskitt & C. L. Skeels, 2004. "Approximating the Distribution of the Instrumental Variables Estimator when the Concentration Parameter is Small," Monash Econometrics and Business Statistics Working Papers 19/04, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. D. S. Poskitt & C. L. Skeels, 2004. "Assessing the Magnitude of the Concentration Parameter in a Simultaneous Equations Model," Monash Econometrics and Business Statistics Working Papers 29/04, Monash University, Department of Econometrics and Business Statistics.
    2. D.S. Poskitt & C.L. Skeels, 2005. "Small Concentration Asymptotics and Instrumental Variables Inference," Department of Economics - Working Papers Series 948, The University of Melbourne.
    3. C.L. Skeels, 2007. "Conceptual Frameworks and Experimental Design in Simultaneous Equations," Department of Economics - Working Papers Series 1020, The University of Melbourne.

  22. D.S. Poskitt & Jing Zhang, 2004. "Estimating Components in Finite Mixtures and Hidden Markov Models," Monash Econometrics and Business Statistics Working Papers 10/04, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Md Atikur Rahman Khan & D.S. Poskitt, 2010. "Description Length Based Signal Detection in singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 13/10, Monash University, Department of Econometrics and Business Statistics.

  23. D. S. Poskitt & C. L. Skeels, 2004. "Assessing the Magnitude of the Concentration Parameter in a Simultaneous Equations Model," Monash Econometrics and Business Statistics Working Papers 29/04, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Poskitt, D.S. & Skeels, C.L., 2007. "Approximating the distribution of the two-stage least squares estimator when the concentration parameter is small," Journal of Econometrics, Elsevier, vol. 139(1), pages 217-236, July.
    2. Don S. Poskitt, 2020. "On GMM Inference: Partial Identification, Identification Strength, and Non-Standard," Monash Econometrics and Business Statistics Working Papers 40/20, Monash University, Department of Econometrics and Business Statistics.
    3. Pham, Vivienne & Prentice, David, 2010. "An empirical analysis of the counterfactual: a merger and divestiture in the Australian cigarette industry," MPRA Paper 26713, University Library of Munich, Germany.
    4. Tchatoka, Firmin Doko, 2015. "Subset Hypotheses Testing And Instrument Exclusion In The Linear Iv Regression," Econometric Theory, Cambridge University Press, vol. 31(6), pages 1192-1228, December.
    5. Matthew C. Harding & Jerry Hausman & Christopher Palmer, 2015. "Finite sample bias corrected IV estimation for weak and many instruments," CeMMAP working papers CWP41/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "The First-stage F Test with Many Weak Instruments," Papers 2302.14423, arXiv.org, revised Sep 2024.

  24. D.S. Poskitt & C.L. Skeels, 2002. "Assessing Instrumental Variable Relevance:An Alternative Measure and Some Exact Finite Sample Theory," Department of Economics - Working Papers Series 862, The University of Melbourne.

    Cited by:

    1. Grant Hillier & Giovanni Forchini, 2004. "Ill-posed Problems and Instruments' Weakness," Econometric Society 2004 Australasian Meetings 357, Econometric Society.
    2. Joseph, Agnes S. & Kiviet, Jan F., 2005. "Viewing the relative efficiency of IV estimators in models with lagged and instantaneous feedbacks," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 417-444, April.
    3. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2007. "Enhanced routines for instrumental variables/generalized method of moments estimation and testing," Stata Journal, StataCorp LP, vol. 7(4), pages 465-506, December.
    4. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
    5. D. S. Poskitt & C. L. Skeels, 2004. "Approximating the Distribution of the Instrumental Variables Estimator when the Concentration Parameter is Small," Monash Econometrics and Business Statistics Working Papers 19/04, Monash University, Department of Econometrics and Business Statistics.
    6. Gönül Çolak, 2010. "Diversification, Refocusing and Firm Value," European Financial Management, European Financial Management Association, vol. 16(3), pages 422-448, June.
    7. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2007. "Enhanced routines for instrumental variables/GMM estimation and testing," Boston College Working Papers in Economics 667, Boston College Department of Economics, revised 05 Sep 2007.

  25. Poskitt, D., 1996. "The Analysis of Cointegrated Autoregressive Moving-Average Systems," SFB 373 Discussion Papers 1996,58, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Bartel, Holger & Lütkepohl, Helmut, 1997. "Estimating the Kronecker indices of cointegrated echelon form VARMA models," SFB 373 Discussion Papers 1997,2, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  26. Lütkepohl, H. & Poskitt, D. S., 1996. "Consistent Estimation of the Number of Cointegration Relations in a Vector Autoregressive Model," SFB 373 Discussion Papers 1996,74, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Shintani, Mototsugu, 2001. "A simple cointegrating rank test without vector autoregression," Journal of Econometrics, Elsevier, vol. 105(2), pages 337-362, December.
    2. George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    3. Lütkepohl, Helmut, 1999. "Vector autoregressions," SFB 373 Discussion Papers 1999,4, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    5. Richard G. Anderson & Hailong Qian & Robert H. Rasche, 2006. "Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators," Working Papers 2006-050, Federal Reserve Bank of St. Louis.
    6. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, September.
    7. Athanasopouolos, George & Poskitt, Don & Vahid, Farshid & Yao, Wenying, 2014. "Forecasting with EC-VARMA models," Working Papers 2014-07, University of Tasmania, Tasmanian School of Business and Economics, revised 22 Feb 2014.
    8. Lütkepohl, Helmut, 1999. "Vector autoregressive analysis," SFB 373 Discussion Papers 1999,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    9. Josheski, Dushko & Lazarov, Darko & Fotov, Risto & Koteski, Cane, 2011. "IS-LM model for US economy: testing in JMULTI," MPRA Paper 34024, University Library of Munich, Germany.

  27. Poskitt, D. & Lütkepohl, H., 1995. "Consistent Specification of Cointegrated Autoregressive Moving-Average Systems," SFB 373 Discussion Papers 1995,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Melard, Guy & Roy, Roch & Saidi, Abdessamad, 2006. "Exact maximum likelihood estimation of structured or unit root multivariate time series models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 2958-2986, July.
    2. Bartel, Holger & Lütkepohl, Helmut, 1997. "Estimating the Kronecker indices of cointegrated echelon form VARMA models," SFB 373 Discussion Papers 1997,2, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Lütkepohl, Helmut, 1999. "Vector autoregressions," SFB 373 Discussion Papers 1999,4, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Asymptotic Distribution of a Simple Linear Estimator for VARMA Models in Echelon Form," Cahiers de recherche 10-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    5. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    6. Christian Kascha & Carsten Trenkler, 2011. "Cointegrated VARMA models and forecasting US interest rates," ECON - Working Papers 033, Department of Economics - University of Zurich.
    7. Lütkepohl, Helmut, 1999. "Vector autoregressive analysis," SFB 373 Discussion Papers 1999,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  28. D.S. Poskitt, "undated". "Specification of echelon form VARMA models," Statistic und Oekonometrie 9305, Humboldt Universitaet Berlin.

    Cited by:

    1. Dupasquier, Chantal & Guay, Alain & St-Amant, Pierre, 1999. "A Survey of Alternative Methodologies for Estimating Potential Output and the Output Gap," Journal of Macroeconomics, Elsevier, vol. 21(3), pages 577-595, July.
    2. Gustavsson, Patrik & Nordström, Jonas, 1999. "The Impact of Seasonal Unit Roots and Vector ARMA Modeling on Forecasting Monthly Tourism Flows," Working Paper Series 150, Trade Union Institute for Economic Research, revised 01 Jul 2000.
    3. George Athanasopoulos & Farshid Vahid, 2008. "A complete VARMA modelling methodology based on scalar components," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 533-554, May.
    4. D. S. Poskitt, 2004. "On The Identification and Estimation of Partially Nonstationary ARMAX Systems," Monash Econometrics and Business Statistics Working Papers 20/04, Monash University, Department of Econometrics and Business Statistics.
    5. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    6. Athanasopoulos, George & Vahid, Farshid, 2008. "VARMA versus VAR for Macroeconomic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 237-252, April.
    7. George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    8. Christian Kascha, 2012. "A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 297-324.
    9. Bartel, Holger & Lütkepohl, Helmut, 1997. "Estimating the Kronecker indices of cointegrated echelon form VARMA models," SFB 373 Discussion Papers 1997,2, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    10. Lütkepohl, Helmut, 1999. "Vector autoregressions," SFB 373 Discussion Papers 1999,4, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    11. Joshua C C Chan & Eric Eisenstat & Gary Koop, 2014. "Large Bayesian VARMAs," Working Papers 1409, University of Strathclyde Business School, Department of Economics.
    12. George Athanasopoulos & D. Poskitt & Farshid Vahid, 2012. "Two Canonical VARMA Forms: Scalar Component Models Vis-à-Vis the Echelon Form," Econometric Reviews, Taylor & Francis Journals, vol. 31(1), pages 60-83.
    13. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    14. José Casals Carro & Alfredo García-Hiernaux & Miguel Jerez, 2010. "From general State-Space to VARMAX models," Documentos de Trabajo del ICAE 1002, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    15. Luis A. Gil-Alana & Rangan Gupta & Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2015. "Time Series Analysis of Persistence in Crude Oil Price Volatility across Bull and Bear Regimes," Working Papers 201580, University of Pretoria, Department of Economics.
    16. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Asymptotic Distribution of a Simple Linear Estimator for VARMA Models in Echelon Form," Cahiers de recherche 10-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    17. René Lalonde, 2000. "Le modèle USM d'analyse et de projection de l'économie américaine," Staff Working Papers 00-19, Bank of Canada.
    18. Alfredo García-Hiernaux, 2009. "Diagnostic checking using subspace methods," Documentos de Trabajo del ICAE 2009-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    19. Lutkepohl, Helmut & Claessen, Holger, 1997. "Analysis of cointegrated VARMA processes," Journal of Econometrics, Elsevier, vol. 80(2), pages 223-239, October.
    20. Dias, Gustavo Fruet & Kapetanios, George, 2018. "Estimation and forecasting in vector autoregressive moving average models for rich datasets," Journal of Econometrics, Elsevier, vol. 202(1), pages 75-91.
    21. Helmut Luetkepohl, 2007. "Econometric Analysis with Vector Autoregressive Models," Economics Working Papers ECO2007/11, European University Institute.
    22. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    23. Vicky Fasen‐Hartmann & Sebastian Kimmig, 2020. "Robust estimation of stationary continuous‐time arma models via indirect inference," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 620-651, September.
    24. D.S. Poskitt, 2004. "Some Results on the Identification and Estimation of Vector ARMAX Processes," Monash Econometrics and Business Statistics Working Papers 12/04, Monash University, Department of Econometrics and Business Statistics.
    25. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    26. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    27. Celina Pestano & Concepción González, 1998. "A new approach in multivariate time series specification," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 4(3), pages 229-242, August.
    28. Bhansali, Rajendra J., 2020. "Model specification and selection for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    29. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, September.
    30. Arino, Miguel A. & Newbold, Paul, 1998. "Computation of the Beveridge-Nelson decomposition for multivariate economic time series," Economics Letters, Elsevier, vol. 61(1), pages 37-42, October.
    31. Alexandra Horobet & Irina Mnohoghitnei & Emanuela Marinela Luminita Zlatea & Lucian Belascu, 2022. "The Interplay between Digitalization, Education and Financial Development: A European Case Study," JRFM, MDPI, vol. 15(3), pages 1-23, March.
    32. Dark, Jonathan, 2018. "Multivariate models with long memory dependence in conditional correlation and volatility," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 162-180.
    33. Athanasopouolos, George & Poskitt, Don & Vahid, Farshid & Yao, Wenying, 2014. "Forecasting with EC-VARMA models," Working Papers 2014-07, University of Tasmania, Tasmanian School of Business and Economics, revised 22 Feb 2014.
    34. Flores de Frutos, Rafael & Serrano, Gregorio R., 1997. "A generalized least squares estimation method for invertible vector moving average models," Economics Letters, Elsevier, vol. 57(2), pages 149-156, December.
    35. Pierre St-Amant & David Tessier, 1998. "A Discussion of the Reliability of Results Obtained with Long-Run Identifying Restrictions," Staff Working Papers 98-4, Bank of Canada.
    36. René Lalonde & Jennifer Page & Pierre St-Amant, 1998. "Une nouvelle méthode d'estimation de l'écart de production et son application aux États-Unis, au Canada et à l'Allemagne," Staff Working Papers 98-21, Bank of Canada.
    37. Galeano, Pedro, 2004. "Variance changes detection in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS ws041305, Universidad Carlos III de Madrid. Departamento de Estadística.
    38. Poskitt, D. S., 2003. "On the specification of cointegrated autoregressive moving-average forecasting systems," International Journal of Forecasting, Elsevier, vol. 19(3), pages 503-519.
    39. Dufour, Jean-Marie & Tessier, David, 1997. "La causalité entre la monnaie et le revenu : une analyse fondée sur un modèle VARMA-échelon," L'Actualité Economique, Société Canadienne de Science Economique, vol. 73(1), pages 351-366, mars-juin.
    40. Lütkepohl, Helmut, 1999. "Vector autoregressive analysis," SFB 373 Discussion Papers 1999,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

Articles

  1. Chuhui Li & Donald S. Poskitt & Frank Windmeijer & Xueyan Zhao, 2022. "Binary outcomes, OLS, 2SLS and IV probit," Econometric Reviews, Taylor & Francis Journals, vol. 41(8), pages 859-876, September.
    See citations under working paper version above.
  2. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.

    Cited by:

    1. Almeida, Thiago Ramos, 2024. "Estimating time-varying factors’ variance in the string-term structure model with stochastic volatility," Research in International Business and Finance, Elsevier, vol. 70(PA).
    2. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    3. Wang, Nianling & Lou, Zhusheng, 2023. "Sequential Bayesian analysis for semiparametric stochastic volatility model with applications," Economic Modelling, Elsevier, vol. 123(C).

  3. Martin, Gael M. & Nadarajah, K. & Poskitt, D.S., 2020. "Issues in the estimation of mis-specified models of fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 215(2), pages 559-573.
    See citations under working paper version above.
  4. Li, Chuhui & Poskitt, D.S. & Zhao, Xueyan, 2019. "The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification," Journal of Econometrics, Elsevier, vol. 209(1), pages 94-113.
    See citations under working paper version above.
  5. Khan, M. Atikur Rahman & Poskitt, D.S., 2017. "Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application," International Journal of Forecasting, Elsevier, vol. 33(1), pages 199-213.

    Cited by:

    1. Miguel de Carvalho & Gabriel Martos, 2022. "Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 167-180, January.
    2. Mahdi Kalantari & Hossein Hassani, 2019. "Automatic Grouping in Singular Spectrum Analysis," Forecasting, MDPI, vol. 1(1), pages 1-16, October.
    3. Gillard, Jonathan & Usevich, Konstantin, 2018. "Structured low-rank matrix completion for forecasting in time series analysis," International Journal of Forecasting, Elsevier, vol. 34(4), pages 582-597.
    4. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    5. Salah L. Zubaidi & Sandra Ortega-Martorell & Patryk Kot & Rafid M. Alkhaddar & Mawada Abdellatif & Sadik K. Gharghan & Maytham S. Ahmed & Khalid Hashim, 2020. "A Method for Predicting Long-Term Municipal Water Demands Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1265-1279, February.
    6. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    7. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.

  6. D. S. Poskitt & Wenying Yao, 2017. "Vector Autoregressions and Macroeconomic Modeling: An Error Taxonomy," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 407-419, July.

    Cited by:

    1. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    2. Yao, Wenying & Kam, Timothy & Vahid, Farshid, 2014. "VAR(MA), what is it good for? more bad news for reduced-form estimation and inference," Working Papers 2014-14, University of Tasmania, Tasmanian School of Business and Economics.
    3. Joshua Chan & Luca Benati & Eric Eisenstat & Gary Koop, 2018. "Identifying Noise Shocks," Working Paper Series 41, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    4. Giovanni Angelini & Marco M. Sorge, 2021. "Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks," Working Papers wp1160, Dipartimento Scienze Economiche, Universita' di Bologna.
    5. Bernd Funovits, 2020. "Identifiability and Estimation of Possibly Non-Invertible SVARMA Models: A New Parametrisation," Papers 2002.04346, arXiv.org, revised Feb 2021.
    6. Adrian Pagan & Tim Robinson, 2019. "Implications of Partial Information for Applied Macroeconomic Modelling," Melbourne Institute Working Paper Series wp2019n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    7. Wickens, Michael R. & Pagan, Adrian, 2019. "Checking if the Straitjacket Fits," CEPR Discussion Papers 14140, C.E.P.R. Discussion Papers.
    8. Bernd Funovits, 2019. "Identification and Estimation of SVARMA models with Independent and Non-Gaussian Inputs," Papers 1910.04087, arXiv.org.
    9. Funovits, Bernd, 2024. "Identifiability and estimation of possibly non-invertible SVARMA Models: The normalised canonical WHF parametrisation," Journal of Econometrics, Elsevier, vol. 241(2).

  7. Poskitt, D. S. & Martin, Gael M. & Grose, Simone D., 2017. "Bias Correction Of Semiparametric Long Memory Parameter Estimators Via The Prefiltered Sieve Bootstrap," Econometric Theory, Cambridge University Press, vol. 33(3), pages 578-609, June.

    Cited by:

    1. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.
    2. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    3. Zhanshou Chen & Yanting Xiao & Fuxiao Li, 2021. "Monitoring memory parameter change-points in long-memory time series," Empirical Economics, Springer, vol. 60(5), pages 2365-2389, May.

  8. George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    See citations under working paper version above.
  9. Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.

    Cited by:

    1. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    2. Luis A. Gil-Alana & Rangan Gupta & Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2015. "Time Series Analysis of Persistence in Crude Oil Price Volatility across Bull and Bear Regimes," Working Papers 201580, University of Pretoria, Department of Economics.
    3. Bernd Funovits, 2020. "Identifiability and Estimation of Possibly Non-Invertible SVARMA Models: A New Parametrisation," Papers 2002.04346, arXiv.org, revised Feb 2021.
    4. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    5. Bernd Funovits, 2019. "Identification and Estimation of SVARMA models with Independent and Non-Gaussian Inputs," Papers 1910.04087, arXiv.org.
    6. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Funovits, Bernd, 2024. "Identifiability and estimation of possibly non-invertible SVARMA Models: The normalised canonical WHF parametrisation," Journal of Econometrics, Elsevier, vol. 241(2).

  10. Poskitt, D.S. & Grose, Simone D. & Martin, Gael M., 2015. "Higher-order improvements of the sieve bootstrap for fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 188(1), pages 94-110.
    See citations under working paper version above.
  11. Poskitt, D. S. & Skeels, C. L., 2013. "Inference in the Presence of Weak Instruments: A Selected Survey," Foundations and Trends(R) in Econometrics, now publishers, vol. 6(1), pages 1-99, August.

    Cited by:

    1. Don S. Poskitt, 2020. "On GMM Inference: Partial Identification, Identification Strength, and Non-Standard," Monash Econometrics and Business Statistics Working Papers 40/20, Monash University, Department of Econometrics and Business Statistics.
    2. Firmin Doko Tchatoka & Wenjie Wang, 2015. "On Bootstrap Validity for Subset Anderson-Rubin Test in IV Regressions," School of Economics and Public Policy Working Papers 2015-01, University of Adelaide, School of Economics and Public Policy.
    3. Firmin Doko Tchatoka & Jean-Marie Dufour, 2016. "Exogeneity tests, weak identification, incomplete models and non-Gaussian distributions: Invariance and finite-sample distributional theory," School of Economics and Public Policy Working Papers 2016-01, University of Adelaide, School of Economics and Public Policy.
    4. Firmin Doko Tchatoka & Lauren Slinger & Virginie Masson, 2020. "Revisiting empirical studies on the liquidity effect: An identication-robust approach," School of Economics and Public Policy Working Papers 2020-02, University of Adelaide, School of Economics and Public Policy.

  12. Md Atikur Rahman Khan & D. S. Poskitt, 2013. "Moment tests for window length selection in singular spectrum analysis of short– and long–memory processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 141-155, March.
    See citations under working paper version above.
  13. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.
    See citations under working paper version above.
  14. George Athanasopoulos & D. Poskitt & Farshid Vahid, 2012. "Two Canonical VARMA Forms: Scalar Component Models Vis-à-Vis the Echelon Form," Econometric Reviews, Taylor & Francis Journals, vol. 31(1), pages 60-83.
    See citations under working paper version above.
  15. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2012. "Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 732-740.
    See citations under working paper version above.
  16. D. S. Poskitt & C. L. Skeels, 2009. "Assessing the magnitude of the concentration parameter in a simultaneous equations model," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 26-44, March.
    See citations under working paper version above.
  17. Poskitt, D.S. & Skeels, C.L., 2008. "Conceptual frameworks and experimental design in simultaneous equations," Economics Letters, Elsevier, vol. 100(1), pages 138-142, July.

    Cited by:

    1. Giovanni Forchini, 2012. "Structural Equations and Invariance," School of Economics Discussion Papers 0312, School of Economics, University of Surrey.
    2. Nicolas Van de Sijpe & Frank Windmeijer, 2021. "On the Power of the Conditional Likelihood Ratio and Related Tests for Weak-Instrument Robust Inference," Economics Papers 2020-W09, Economics Group, Nuffield College, University of Oxford.

  18. D. S. Poskitt, 2008. "Properties of the Sieve Bootstrap for Fractionally Integrated and Non‐Invertible Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(2), pages 224-250, March.
    See citations under working paper version above.
  19. D. Poskitt, 2007. "Autoregressive approximation in nonstandard situations: the fractionally integrated and non-invertible cases," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(4), pages 697-725, December.

    Cited by:

    1. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    2. Wang, Cindy Shin-Huei & Bauwens, Luc & Hsiao, Cheng, 2013. "Forecasting a long memory process subject to structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 171-184.
    3. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
    4. Demetrescu, Matei & Salish, Nazarii, 2024. "(Structural) VAR models with ignored changes in mean and volatility," International Journal of Forecasting, Elsevier, vol. 40(2), pages 840-854.
    5. Mayer, Alexander, 2020. "(Consistently) testing strict exogeneity against the alternative of predeterminedness in linear time-series models," Economics Letters, Elsevier, vol. 193(C).
    6. Baillie, Richard T. & Kongcharoen, Chaleampong & Kapetanios, George, 2012. "Prediction from ARFIMA models: Comparisons between MLE and semiparametric estimation procedures," International Journal of Forecasting, Elsevier, vol. 28(1), pages 46-53.
    7. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    8. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    9. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.
    10. Dietmar Bauer & Alex Maynard, 2010. "Persistence-robust Granger causality testing," Working Papers 1011, University of Guelph, Department of Economics and Finance.
    11. Rupasinghe, Maduka & Samaranayake, V.A., 2012. "Asymptotic properties of sieve bootstrap prediction intervals for FARIMA processes," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2108-2114.
    12. Zacharias Psaradakis & Marian Vavra, 2017. "Normality Tests for Dependent Data," Working and Discussion Papers WP 12/2017, Research Department, National Bank of Slovakia.
    13. Marian Vavra, 2018. "Assessing Distributional Properties of Forecast Errors," Working and Discussion Papers WP 3/2018, Research Department, National Bank of Slovakia.
    14. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2013. "Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 25/13, Monash University, Department of Econometrics and Business Statistics.
    15. Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2023. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Empirical Economics, Springer, vol. 64(6), pages 2911-2937, June.
    16. Zacharias Psaradakis & Marian Vavra, 2018. "Bootstrap Assisted Tests of Symmetry for Dependent Data," Working and Discussion Papers WP 5/2018, Research Department, National Bank of Slovakia.
    17. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    18. Wang, Shin-Huei & Vasilakis, Chrysovalantis, 2013. "Recursive predictive tests for structural change of long-memory ARFIMA processes with unknown break points," Economics Letters, Elsevier, vol. 118(2), pages 389-392.
    19. Papailias, Fotis & Fruet Dias, Gustavo, 2015. "Forecasting long memory series subject to structural change: A two-stage approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1056-1066.
    20. Bauer, Dietmar & Maynard, Alex, 2012. "Persistence-robust surplus-lag Granger causality testing," Journal of Econometrics, Elsevier, vol. 169(2), pages 293-300.
    21. Zacharias Psaradakis & Marián Vávra, 2015. "A Distance Test of Normality for a Wide Class of Stationary Processes," Birkbeck Working Papers in Economics and Finance 1513, Birkbeck, Department of Economics, Mathematics & Statistics.
    22. Hassler, Uwe, 2012. "Impulse responses of antipersistent processes," Economics Letters, Elsevier, vol. 116(3), pages 454-456.
    23. S. D. Grose & D. S. Poskitt, 2006. "The Finite-Sample Properties of Autoregressive Approximations of Fractionally-Integrated and Non-Invertible Processes," Monash Econometrics and Business Statistics Working Papers 15/06, Monash University, Department of Econometrics and Business Statistics.
    24. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
    25. ChaeWon Baek & Byoungchan Lee, 2022. "A Guide to Autoregressive Distributed Lag Models for Impulse Response Estimations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1101-1122, October.
    26. George Kapetanios & Zacharias Psaradakis, 2016. "Semiparametric Sieve-Type Generalized Least Squares Inference," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 951-985, June.

  20. Poskitt, D.S. & Skeels, C.L., 2007. "Approximating the distribution of the two-stage least squares estimator when the concentration parameter is small," Journal of Econometrics, Elsevier, vol. 139(1), pages 217-236, July.

    Cited by:

    1. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    2. C.L. Skeels, 2007. "Conceptual Frameworks and Experimental Design in Simultaneous Equations," Department of Economics - Working Papers Series 1020, The University of Melbourne.

  21. Poskitt, D.S., 2006. "On The Identification And Estimation Of Nonstationary And Cointegrated Armax Systems," Econometric Theory, Cambridge University Press, vol. 22(6), pages 1138-1175, December.

    Cited by:

    1. D.S. Poskitt, 2009. "Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory," Monash Econometrics and Business Statistics Working Papers 12/09, Monash University, Department of Econometrics and Business Statistics.
    2. Dietmar Bauer & Lukas Matuschek & Patrick de Matos Ribeiro & Martin Wagner, 2020. "A Parameterization of Models for Unit Root Processes: Structure Theory and Hypothesis Testing," Econometrics, MDPI, vol. 8(4), pages 1-54, November.
    3. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    4. Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.
    5. Christian Kascha & Carsten Trenkler, 2011. "Cointegrated VARMA models and forecasting US interest rates," ECON - Working Papers 033, Department of Economics - University of Zurich.
    6. Christis Katsouris, 2024. "Robust Estimation in Network Vector Autoregression with Nonstationary Regressors," Papers 2401.04050, arXiv.org.
    7. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).

  22. D. S. Poskitt, 2005. "A Note on the Specification and Estimation of ARMAX Systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 157-183, March.

    Cited by:

    1. Mélard, Guy, 2022. "An indirect proof for the asymptotic properties of VARMA model estimators," Econometrics and Statistics, Elsevier, vol. 21(C), pages 96-111.
    2. D.S. Poskitt, 2009. "Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory," Monash Econometrics and Business Statistics Working Papers 12/09, Monash University, Department of Econometrics and Business Statistics.
    3. Guy Melard, 2020. "An Indirect Proof for the Asymptotic Properties of VARMA Model Estimators," Working Papers ECARES 2020-10, ULB -- Universite Libre de Bruxelles.
    4. Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.

  23. D. Harris & D. S. Poskitt, 2004. "Determination of cointegrating rank in partially non-stationary processes via a generalised von-Neumann criterion," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 191-217, June.

    Cited by:

    1. Abry, Patrice & Didier, Gustavo, 2018. "Wavelet eigenvalue regression for n-variate operator fractional Brownian motion," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 75-104.
    2. Karsten Reichold, 2022. "A Residuals-Based Nonparametric Variance Ratio Test for Cointegration," Papers 2211.06288, arXiv.org, revised Dec 2022.
    3. Zhang, Rongmao & Robinson, Peter & Yao, Qiwei, 2019. "Identifying cointegration by eigenanalysis," LSE Research Online Documents on Economics 87431, London School of Economics and Political Science, LSE Library.
    4. Ye Cai & Mototsugu Shintani, 2005. "On the Long-Run Variance Ratio Test for a Unit Root," Vanderbilt University Department of Economics Working Papers 0506, Vanderbilt University Department of Economics.
    5. Sella Lisa, 2008. "Old and New Spectral Techniques for Economic Time Series," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200809, University of Turin.

  24. Poskitt, D. S., 2003. "On the specification of cointegrated autoregressive moving-average forecasting systems," International Journal of Forecasting, Elsevier, vol. 19(3), pages 503-519.

    Cited by:

    1. Trenkler, Carsten & Weber, Enzo, 2012. "Identifying the Shocks behind Business Cycle Asynchrony in Euroland," University of Regensburg Working Papers in Business, Economics and Management Information Systems 466, University of Regensburg, Department of Economics.
    2. D. S. Poskitt, 2004. "On The Identification and Estimation of Partially Nonstationary ARMAX Systems," Monash Econometrics and Business Statistics Working Papers 20/04, Monash University, Department of Econometrics and Business Statistics.
    3. George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    4. D.S. Poskitt, 2009. "Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory," Monash Econometrics and Business Statistics Working Papers 12/09, Monash University, Department of Econometrics and Business Statistics.
    5. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Helmut Luetkepohl, 2007. "Econometric Analysis with Vector Autoregressive Models," Economics Working Papers ECO2007/11, European University Institute.
    8. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    9. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    10. Christian Kascha & Carsten Trenkler, 2011. "Cointegrated VARMA models and forecasting US interest rates," ECON - Working Papers 033, Department of Economics - University of Zurich.
    11. Athanasopouolos, George & Poskitt, Don & Vahid, Farshid & Yao, Wenying, 2014. "Forecasting with EC-VARMA models," Working Papers 2014-07, University of Tasmania, Tasmanian School of Business and Economics, revised 22 Feb 2014.

  25. Poskitt, Don S, 2000. "Strongly Consistent Determination of Cointegrating Rank via Canonical Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 77-90, January.

    Cited by:

    1. D. S. Poskitt, 2004. "On The Identification and Estimation of Partially Nonstationary ARMAX Systems," Monash Econometrics and Business Statistics Working Papers 20/04, Monash University, Department of Econometrics and Business Statistics.
    2. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.
    3. George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    4. Al-Sadoon, Majid M., 2014. "Geometric and long run aspects of Granger causality," Journal of Econometrics, Elsevier, vol. 178(P3), pages 558-568.
    5. Bauer, Dietmar & Wagner, Martin, 2002. "Estimating cointegrated systems using subspace algorithms," Journal of Econometrics, Elsevier, vol. 111(1), pages 47-84, November.
    6. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
    7. D.S. Poskitt, 2009. "Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory," Monash Econometrics and Business Statistics Working Papers 12/09, Monash University, Department of Econometrics and Business Statistics.
    8. George Kapetanios, 2003. "A New Nonparametric Test of Cointegration Rank," Working Papers 482, Queen Mary University of London, School of Economics and Finance.
    9. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    10. Alfredo Garcia Hiernaux & Miguel Jerez & José Casals, 2005. "Unit Roots and Cointegrating Matrix Estimation using Subspace Methods," Documentos de Trabajo del ICAE 0512, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    11. D. S. Poskitt, 2005. "Autoregressive Approximation in Nonstandard Situations: The Non-Invertible and Fractionally Integrated Cases," Monash Econometrics and Business Statistics Working Papers 16/05, Monash University, Department of Econometrics and Business Statistics.
    12. Md Atikur Rahman Khan & D.S. Poskitt, 2011. "Window Length Selection and Signal-Noise Separation and Reconstruction in Singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 23/11, Monash University, Department of Econometrics and Business Statistics.
    13. D. Poskitt, 2007. "Autoregressive approximation in nonstandard situations: the fractionally integrated and non-invertible cases," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(4), pages 697-725, December.
    14. D.S. Poskitt, 2016. "Singular Spectrum Analysis of Grenander Processes and Sequential Time Series Reconstruction," Monash Econometrics and Business Statistics Working Papers 15/16, Monash University, Department of Econometrics and Business Statistics.
    15. Heaney, Richard, 2002. "Does knowledge of the cost of carry model improve commodity futures price forecasting ability?: A case study using the London Metal Exchange lead contract," International Journal of Forecasting, Elsevier, vol. 18(1), pages 45-65.
    16. Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.
    17. M. Atikur Rahman Khan & D.S. Poskitt, 2014. "On The Theory and Practice of Singular Spectrum Analysis Forecasting," Monash Econometrics and Business Statistics Working Papers 3/14, Monash University, Department of Econometrics and Business Statistics.
    18. Alfredo García Hiernaux & Miguel Jerez & José Casals, 2005. "Deteccióon de Raíces Unitarias y Cointegración mediante Métodos de Subespacios," Documentos de Trabajo del ICAE 0503, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    19. Athanasopouolos, George & Poskitt, Don & Vahid, Farshid & Yao, Wenying, 2014. "Forecasting with EC-VARMA models," Working Papers 2014-07, University of Tasmania, Tasmanian School of Business and Economics, revised 22 Feb 2014.
    20. Martin Wagner, 2004. "A Comparison of Johansen's, Bierens’ and the Subspace Algorithm Method for Cointegration Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 399-424, July.
    21. Khan, M. Atikur Rahman & Poskitt, D.S., 2017. "Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application," International Journal of Forecasting, Elsevier, vol. 33(1), pages 199-213.
    22. Guillermo Carlomagno & Antoni Espasa, 2021. "Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 641-662, June.
    23. Poskitt, D. S., 2003. "On the specification of cointegrated autoregressive moving-average forecasting systems," International Journal of Forecasting, Elsevier, vol. 19(3), pages 503-519.

  26. Lütkepohl, Helmut & POSKITT, D.S., 1996. "Testing for Causation Using Infinite Order Vector Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 12(1), pages 61-87, March.

    Cited by:

    1. Trenkler, Carsten & Weber, Enzo, 2012. "Identifying the Shocks behind Business Cycle Asynchrony in Euroland," University of Regensburg Working Papers in Business, Economics and Management Information Systems 466, University of Regensburg, Department of Economics.
    2. DUFOUR, Jean-Marie & PELLETIER, Denis & RENAULT, Éric, 2003. "Short Run and Long Run Causality in Time Series : Inference," Cahiers de recherche 14-2003, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    4. Siliti jr Hammadi & Ben mbarek jr Hassene, 2013. "Shocks Transmission in the Mediterranean Zone," Economics Bulletin, AccessEcon, vol. 33(2), pages 1010-1028.
    5. Lee, Yoon-Jin & Okui, Ryo & Shintani, Mototsugu, 2018. "Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes," Journal of Econometrics, Elsevier, vol. 204(2), pages 147-158.
    6. Lütkepohl, Helmut, 1999. "Vector autoregressions," SFB 373 Discussion Papers 1999,4, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. Andrew Rennison, 2003. "Comparing Alternative Output-Gap Estimators: A Monte Carlo Approach," Staff Working Papers 03-8, Bank of Canada.
    8. Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
    9. Mili, Mehdi & Sahut, Jean-Michel & Teulon, Frédéric, 2012. "Non linear and asymmetric linkages between real growth in the Euro area and global financial market conditions: New evidence," Economic Modelling, Elsevier, vol. 29(3), pages 734-741.
    10. Cleiton Guollo Taufemback, 2023. "Non‐parametric short‐ and long‐run Granger causality testing in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 69-92, January.
    11. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
    12. Bai, Zhidong & Wong, Wing-Keung & Zhang, Bingzhi, 2010. "Multivariate linear and nonlinear causality tests," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 5-17.
    13. Serguei Zernov & Victoria Zindle-Walsh & John Galbraith, 2006. "Asymptotics For Estimation Of Truncated Infinite-Dimensional Quantile Regressions," Departmental Working Papers 2006-16, McGill University, Department of Economics.
    14. Josheski, Dushko & Lazarov, Darko, 2011. "Labor market and natural rate of unemployment in US and Canadian time series analysis," MPRA Paper 34685, University Library of Munich, Germany.
    15. Jonathan B. Hill, 2004. "Efficient Tests of Long-Run Causation in Trivariate VAR Processes with a Rolling Window Study of the Money-Income Relationship," Macroeconomics 0407013, University Library of Munich, Germany, revised 15 Feb 2006.
    16. Hidalgo, J., 2005. "A bootstrap causality test for covariance stationary processes," Journal of Econometrics, Elsevier, vol. 126(1), pages 115-143, May.
    17. Jean-michel Sahut & Medhi Mili & Frédéric Teulon, 2012. "What is the linkage between real growth in the Euro area and global financial market conditions?," Economics Bulletin, AccessEcon, vol. 32(3), pages 2464-2480.
    18. Lutkepohl, Helmut & Saikkonen, Pentti, 1997. "Impulse response analysis in infinite order cointegrated vector autoregressive processes," Journal of Econometrics, Elsevier, vol. 81(1), pages 127-157, November.
    19. Javier Hidalgo, 2003. "A Bootstrap Causality Test for Covariance Stationary Processes," STICERD - Econometrics Paper Series 462, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    20. Helmut Luetkepohl, 2007. "Econometric Analysis with Vector Autoregressive Models," Economics Working Papers ECO2007/11, European University Institute.
    21. Jonathan B. Hill, 2004. "Causation Delays and Causal Neutralization for General Horizons: The Money-Output Relationship Revisited," Econometrics 0402002, University Library of Munich, Germany, revised 23 Mar 2005.
    22. Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
    23. Ekaterini Panopoulou & Nikitas Pittis & Sarantis Kalyvitis, 2006. "Looking far in the past: Revisiting the growth-returns nexus with non-parametric tests," The Institute for International Integration Studies Discussion Paper Series iiisdp134, IIIS.
    24. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    25. Zernov, Serguei & Zinde-Walsh, Victoria & Galbraith, John W., 2009. "Asymptotics for estimation of quantile regressions with truncated infinite-dimensional processes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 497-508, March.
    26. Hamdi El Asli & Lakhmaiss Hamid & Afif Zineb & Azeroual Mohamed, 2024. "Impact of, Human Capital, Economic Factors, Energy Consumption, and Urban Growth on Environmental Sustainability in Morocco: An ARDL Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 656-668, March.
    27. Benkwitz, Alexander & Lütkepohl, Helmut & Neumann, Michael H., 1997. "Problems related to bootstrapping impulse responses of autoregressive processes," SFB 373 Discussion Papers 1997,85, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    28. Christis Hassapis, 2003. "Financial variables and real activity in Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 36(2), pages 421-442, May.
    29. Fathali Firoozi & Donald Lien, 2011. "A Procedure for Testing Granger Causality of Infinite Order," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 10(2), pages 165-170, August.
    30. Hidalgo, Javier, 2003. "A bootstrap causality test for covariance stationary processes," LSE Research Online Documents on Economics 6848, London School of Economics and Political Science, LSE Library.
    31. Horowitz, Joel L. & Savin, N. E., 2000. "Empirically relevant critical values for hypothesis tests: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 95(2), pages 375-389, April.
    32. Lütkepohl, Helmut & Saikkonen, Pentti, 1997. "Order selection in testing for the cointegrating rank of a VAR process," SFB 373 Discussion Papers 1997,93, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    33. Dufour, Jean-Marie & Tessier, David, 1997. "La causalité entre la monnaie et le revenu : une analyse fondée sur un modèle VARMA-échelon," L'Actualité Economique, Société Canadienne de Science Economique, vol. 73(1), pages 351-366, mars-juin.
    34. Lütkepohl, Helmut, 1999. "Vector autoregressive analysis," SFB 373 Discussion Papers 1999,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    35. Josheski, Dushko & Lazarov, Darko & Fotov, Risto & Koteski, Cane, 2011. "IS-LM model for US economy: testing in JMULTI," MPRA Paper 34024, University Library of Munich, Germany.

  27. Lutkepohl, Helmut & Poskitt, D S, 1996. "Specification of Echelon-Form VARMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 69-79, January.
    See citations under working paper version above.
  28. D. S. Poskitt & M. O. Salau, 1995. "On The Relationship Between Generalized Least Squares And Gaussian Estimation Of Vector Arma Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(6), pages 617-645, November.

    Cited by:

    1. Ursu, Eugen & Duchesne, Pierre, 2009. "On multiplicative seasonal modelling for vector time series," Statistics & Probability Letters, Elsevier, vol. 79(19), pages 2045-2052, October.
    2. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    3. M. Salau, 2003. "The effects of different choices of order for autoregressive approximation on the Gaussian likelihood estimates for ARMA models," Statistical Papers, Springer, vol. 44(1), pages 89-105, January.
    4. Salau, M. O., 1998. "Efficient computation of zeros of the moving average operator with real matricial coefficients," SFB 373 Discussion Papers 1998,46, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    6. Dufour, Jean-Marie & Jouini, Tarek, 2014. "Asymptotic distributions for quasi-efficient estimators in echelon VARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 69-86.
    7. Pierre Duchesne & Pierre Lafaye de Micheaux, 2013. "Distributions for residual autocovariances in parsimonious periodic vector autoregressive models with applications," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 496-507, July.
    8. Poskitt, D. S., 2003. "On the specification of cointegrated autoregressive moving-average forecasting systems," International Journal of Forecasting, Elsevier, vol. 19(3), pages 503-519.

  29. Poskitt, D.S., 1994. "A Note on Autoregressive Modeling," Econometric Theory, Cambridge University Press, vol. 10(5), pages 884-899, December.

    Cited by:

    1. Medel, Carlos A., 2012. "¿Akaike o Schwarz? ¿Cuál elegir para predecir el PIB chileno? [Akaike or Schwarz? Which One is a Better Predictor of Chilean GDP?]," MPRA Paper 35950, University Library of Munich, Germany.
    2. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.
    3. Stephan Smeekes, 2013. "Detrending Bootstrap Unit Root Tests," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 869-891, November.
    4. Datta Gupta, Syamantak & Mazumdar, Ravi R. & Glynn, Peter, 2013. "On the convergence of the spectrum of finite order approximations of stationary time series," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 1-21.
    5. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2013. "Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 25/13, Monash University, Department of Econometrics and Business Statistics.
    6. Dmitriy Ivanov & Zaineb Yakoub, 2023. "Overview of Identification Methods of Autoregressive Model in Presence of Additive Noise," Mathematics, MDPI, vol. 11(3), pages 1-21, January.
    7. ChaeWon Baek & Byoungchan Lee, 2022. "A Guide to Autoregressive Distributed Lag Models for Impulse Response Estimations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1101-1122, October.
    8. Camba-Méndez, Gonzalo, 2020. "On the inflation risks embedded in sovereign bond yields," Working Paper Series 2423, European Central Bank.

  30. Poskitt, D. S. & Salau, M. O., 1994. "On the Asymptotic Relative Efficiency of Gaussian and Least Squares Estimators for Vector ARMA Models," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 294-317, November.

    Cited by:

    1. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    2. D. S. Poskitt, 2005. "A Note on the Specification and Estimation of ARMAX Systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 157-183, March.
    3. D. S. Poskitt & M. O. Salau, 1995. "On The Relationship Between Generalized Least Squares And Gaussian Estimation Of Vector Arma Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(6), pages 617-645, November.
    4. D.S. Poskitt, 2004. "Some Results on the Identification and Estimation of Vector ARMAX Processes," Monash Econometrics and Business Statistics Working Papers 12/04, Monash University, Department of Econometrics and Business Statistics.

  31. Lütkepohl, Helmut & Poskitt, D.S., 1991. "Estimating Orthogonal Impulse Responses via Vector Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 7(4), pages 487-496, December.

    Cited by:

    1. Alastair R. Hall & Atsushi Inoue & James M Nason & Barbara Rossi, 2009. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Centre for Growth and Business Cycle Research Discussion Paper Series 127, Economics, The University of Manchester.
    2. Lee, Yoon-Jin & Okui, Ryo & Shintani, Mototsugu, 2018. "Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes," Journal of Econometrics, Elsevier, vol. 204(2), pages 147-158.
    3. Sinan Q. Salih & Intisar Alakili & Ufuk Beyaztas & Shamsuddin Shahid & Zaher Mundher Yaseen, 2021. "Prediction of dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand using hydrometeorological variables: case study of Selangor River, Malaysia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 8027-8046, May.
    4. Galariotis, Emilios & Makrichoriti, Panagiota & Spyrou, Spyros, 2018. "The impact of conventional and unconventional monetary policy on expectations and sentiment," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 1-20.
    5. Marcellino, Massimiliano & Jordà , Òscar, 2008. "Path Forecast Evaluation," CEPR Discussion Papers 7009, C.E.P.R. Discussion Papers.
    6. Qadan, Mahmoud & Idilbi-Bayaa, Yasmeen, 2020. "Risk appetite and oil prices," Energy Economics, Elsevier, vol. 85(C).
    7. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    8. Emilios C. Galariotis & Panagiota Makrichoriti & Spyros Spyrou, 2016. "Sovereign CDS Spread Determinants and Spill-Over Effects During Financial Crisis: A Panel VAR Approach," Post-Print hal-01358715, HAL.
    9. Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021. "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 863-882, December.
    10. Dewi Sovia & Abd. Majid M. Shabri & Aliasuddin & Kassim Salina, 2018. "Dynamics of Financial Development, Economic Growth, and Poverty Alleviation: The Indonesian Experience," South East European Journal of Economics and Business, Sciendo, vol. 13(1), pages 17-30, June.
    11. Bergman, Michael, 1996. "International evidence on the sources of macroeconomic fluctuations," European Economic Review, Elsevier, vol. 40(6), pages 1237-1258, June.
    12. Izatov, Asset, 2015. "The Role of Oil Prices, Real Effective Exchange Rate and Inflation in Economic Activity of Russia: An Empirical Investigation," MPRA Paper 70735, University Library of Munich, Germany, revised 2015.
    13. John Galbraith & Aman Ullah & Victoria Zinde-Walsh, 2002. "Estimation Of The Vector Moving Average Model By Vector Autoregression," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 205-219.
    14. Lutkepohl, Helmut & Saikkonen, Pentti, 1997. "Impulse response analysis in infinite order cointegrated vector autoregressive processes," Journal of Econometrics, Elsevier, vol. 81(1), pages 127-157, November.
    15. Akadiri, Ada Chigozie & Akadiri, Seyi Saint & Gungor, Hasan, 2019. "The role of natural gas consumption in Saudi Arabia's output and its implication for trade and environmental quality," Energy Policy, Elsevier, vol. 129(C), pages 230-238.
    16. Oscar Jorda, 2007. "Inference for Impulse Responses," Working Papers 201, University of California, Davis, Department of Economics.
    17. Inoue, Atsushi & Kilian, Lutz, 2016. "Joint confidence sets for structural impulse responses," Journal of Econometrics, Elsevier, vol. 192(2), pages 421-432.
    18. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    19. Liu, Philip & Theodoridis, Konstantinos, 2010. "DSGE model restrictions for structural VAR identification," Bank of England working papers 402, Bank of England.
    20. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, September.
    21. Asset Izatov, 2015. "The Role of Oil Prices, the Real Effective Exchange Rate, and Inflation in Economic Activity of Russia: An Empirical Investigation," Eastern European Business and Economics Journal, Eastern European Business and Economics Studies Centre, vol. 1(3), pages 48-70.
    22. Kundu, Srikanta & Paul, Amartya, 2022. "Effect of economic policy uncertainty on stock market return and volatility under heterogeneous market characteristics," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 597-612.
    23. Josheski, Dushko & Lazarov, Darko & Fotov, Risto & Koteski, Cane, 2011. "IS-LM model for US economy: testing in JMULTI," MPRA Paper 34024, University Library of Munich, Germany.
    24. Vesna Bucevska & Borjan Gjelevski & Lea Matevska, 2023. "Oil Prices And Their Long-Term Relationship With Macroeconomic And Financial Indicators," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 21(1), pages 3-24, May.

  32. M. S. Mackisack & D. S. Poskitt, 1990. "Some Properties Of Autoregressive Estimates For Processes With Mixed Spectra," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(4), pages 325-337, July.

    Cited by:

    1. Charles Kooperberg & Charles J. Stone & Young K. Truong, 1995. "Logspline Estimation Of A Possibly Mixed Spectral Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(4), pages 359-388, July.
    2. Chen, Bei & Gel, Yulia R., 2010. "Autoregressive frequency detection using Regularized Least Squares," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1712-1727, August.

  33. Poskitt, D. S. & Tremayne, A. R., 1986. "The selection and use of linear and bilinear time series models," International Journal of Forecasting, Elsevier, vol. 2(1), pages 101-114.

    Cited by:

    1. Rossen, Anja, 2014. "On the predictive content of nonlinear transformations of lagged autoregression residuals and time series observations," HWWI Research Papers 157, Hamburg Institute of International Economics (HWWI).
    2. Philip Hans Franses, 2019. "Model‐based forecast adjustment: With an illustration to inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(2), pages 73-80, March.
    3. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    4. Liu, Yamei, 2000. "Overfitting and forecasting: linear versus non-linear time series models," ISU General Staff Papers 2000010108000014914, Iowa State University, Department of Economics.
    5. Filelis - Papadopoulos, Christos K. & Kyziropoulos, Panagiotis E. & Morrison, John P. & O‘Reilly, Philip, 2022. "Modelling and forecasting based on recursive incomplete pseudoinverse matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 358-376.
    6. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
    7. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    8. Yang, Hu & Chen, Yu & Chen, Kedong & Wang, Haijun, 2024. "Temporal-spatial dependencies enhanced deep learning model for time series forecast," International Review of Financial Analysis, Elsevier, vol. 94(C).

  34. D. S. Poskitt & A. R. Tremayne, 1986. "Some Aspects Of The Performance Of Diagnostic Checks In Bivariate Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 217-233, May.

    Cited by:

    1. Robert C. Jung & A. R. Tremayne, 2003. "Testing for serial dependence in time series models of counts," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 65-84, January.
    2. D. S. Poskitt, 2005. "A Note on the Specification and Estimation of ARMAX Systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 157-183, March.
    3. D. S. Poskitt & M. O. Salau, 1995. "On The Relationship Between Generalized Least Squares And Gaussian Estimation Of Vector Arma Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(6), pages 617-645, November.
    4. D.S. Poskitt, 2004. "Some Results on the Identification and Estimation of Vector ARMAX Processes," Monash Econometrics and Business Statistics Working Papers 12/04, Monash University, Department of Econometrics and Business Statistics.

  35. D. S. Poskitt & A. R. Tremayne, 1981. "A Time Series Application Of The Use Of Monte Carlo Methods To Compare Statistical Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(4), pages 263-277, July.

    Cited by:

    1. Neil R. Ericsson, 1987. "Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses," International Finance Discussion Papers 317, Board of Governors of the Federal Reserve System (U.S.).

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