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Forecasting with Spatial Panel Data

Citations

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Cited by:

  1. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
  2. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
  3. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
  4. Giovanni Millo, 2022. "The generalized spatial random effects model in R," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-18, December.
  5. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
  6. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
  7. Matías Mayor & Roberto Patuelli, 2015. "Spatial panel data forecasting over different horizons, cross-sectional and temporal dimensions," Revue d'économie régionale et urbaine, Armand Colin, vol. 0(1), pages 149-180.
  8. Wongsa-art, Pipat & Kim, Namhyun & Xia, Yingcun & Moscone, Francesco, 2024. "Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England," Regional Science and Urban Economics, Elsevier, vol. 106(C).
  9. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
  10. A. M. Angulo & J. Mur & F. J. Trívez, 2018. "Measuring resilience to economic shocks: an application to Spain," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(2), pages 349-373, March.
  11. Hughes, Gordon & Chinowsky, Paul & Strzepek, Ken, 2010. "The costs of adaptation to climate change for water infrastructure in OECD countries," Utilities Policy, Elsevier, vol. 18(3), pages 142-153, September.
  12. W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Understanding spatial heterogeneity in GB agricultural land-use for improved policy targeting," Cardiff Economics Working Papers E2021/8, Cardiff University, Cardiff Business School, Economics Section.
  13. Bernard Fingleton, 2014. "Forecasting with dynamic spatial panel data: practical implementation methods," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 194-207.
  14. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
  15. Xiaohong Chen & Xiang Wang & Hua Zhang & Jia Li, 2014. "The Diversity and Evolution Process of Bus System Performance in Chinese Cities: An Empirical Study," Sustainability, MDPI, vol. 6(11), pages 1-17, November.
  16. Roberto Patuelli & Matías Mayor, 2014. "Introduction," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 191-193.
  17. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
  18. Fonseca Morello, Thiago & Marchetti Ramos, Rossano & O. Anderson, Liana & Owen, Nathan & Rosan, Thais Michele & Steil, Lara, 2020. "Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon," Ecological Economics, Elsevier, vol. 169(C).
  19. W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Modeling and predicting agricultural land use in England based on spatially high-resolution data," Cardiff Economics Working Papers E2021/7, Cardiff University, Cardiff Business School, Economics Section.
  20. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
  21. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
  22. Lung‐fei Lee & Jihai Yu, 2012. "Spatial Panels: Random Components Versus Fixed Effects," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1369-1412, November.
  23. Xueting Zhao & J. Burnett, 2014. "Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 171-183, October.
  24. Yu, Dalei & Bai, Peng & Ding, Chang, 2015. "Adjusted quasi-maximum likelihood estimator for mixed regressive, spatial autoregressive model and its small sample bias," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 116-135.
  25. Alejandro Barragán-Ocaña & Gerardo Reyes-Ruiz & Samuel Olmos-Peña & Hortensia Gómez-Viquez, 2020. "Approach to the identification of an alternative technological innovation index," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 23-45, January.
  26. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
  27. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
  28. B. Fingleton & P. Cheshire & H. Garretsen & D. Igliori & J. Le Gallo & P. McCann & J. McCombie & V. Monastiriotis & B. Moore & M. Roberts, 2011. "Editorial," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(3), pages 243-248, September.
  29. You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 652-668.
  30. Manami Ogura, 2022. "Forecasting consumption expenditure using a dynamic panel model with cross-sectional dependence: the case of Japan," SN Business & Economics, Springer, vol. 2(9), pages 1-16, September.
  31. Marcos-Martinez, Raymundo & Bryan, Brett A. & Schwabe, Kurt A. & Connor, Jeffery D. & Law, Elizabeth A., 2018. "Forest transition in developed agricultural regions needs efficient regulatory policy," Forest Policy and Economics, Elsevier, vol. 86(C), pages 67-75.
  32. Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.
  33. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
  34. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
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