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Model Selection In Periodic Autoregressions

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  • Philip Hans Franses
  • Richard Paap
Abstract
In this paper, the authors analyze a model selection strategy for periodic autoregressive time-series processes. It involves autoregressive order selection and tests for unit roots at the (non-)seasonal frequencies. The strategy is evaluated using Monte Carlo replications and it is applied to several quarterly U.K. macroeconomic time series. Copyright 1994 by Blackwell Publishing Ltd
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Philip Hans Franses & Richard Paap, 1994. "Model Selection In Periodic Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(4), pages 421-439, November.
    • Handle: RePEc:bla:obuest:v:56:y:1994:i:4:p:421-439
    DOI: j.1468-0084.1994.tb00018.x
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    Cited by:

    1. Franses, Ph.H.B.F. & Paap, R., 1999. "Forecasting with periodic autoregressive time series models," Econometric Institute Research Papers EI 9927-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    3. Albertson, Kevin & Aylen, Jonathan, 1996. "Modelling the Great Lakes freeze: forecasting and seasonality in the market for ferrous scrap," International Journal of Forecasting, Elsevier, vol. 12(3), pages 345-359, September.
    4. Clements, Michael & Smith, Jeremy, 1997. "Forecasting Seasonal Uk Consumption Components," Economic Research Papers 268761, University of Warwick - Department of Economics.
    5. Herwartz, Helmut, 1997. "Performance of periodic error correction models in forecasting consumption data," International Journal of Forecasting, Elsevier, vol. 13(3), pages 421-431, September.
    6. Christiano, Lawrence J. & Todd, Richard M., 2002. "The conventional treatment of seasonality in business cycle analysis: does it create distortions?," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 335-364, March.
    7. Tomas Barrio Castro & Mariam Camarero & Cecilio Tamarit, 2015. "An analysis of the trade balance for OECD countries using periodic integration and cointegration," Empirical Economics, Springer, vol. 49(2), pages 389-402, September.
    8. Franses, Philip Hans & Ooms, Marius, 1997. "A periodic long-memory model for quarterly UK inflation," International Journal of Forecasting, Elsevier, vol. 13(1), pages 117-126, March.
    9. Philip Hans Franses & Richard Paap, 2011. "Random‐coefficient periodic autoregressions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(1), pages 101-115, February.
    10. Boswijk, H. Peter & Franses, Philip Hans & Haldrup, Niels, 1997. "Multiple unit roots in periodic autoregression," Journal of Econometrics, Elsevier, vol. 80(1), pages 167-193, September.
    11. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    12. Breitung, Jorg & Franses, Philip Hans, 1997. "Impulse response functions for periodic integration," Economics Letters, Elsevier, vol. 55(1), pages 35-40, August.
    13. Evans, Mark, 2006. "A study of the relationship between regional ferrous scrap prices in the USA, 1958-2004," Resources Policy, Elsevier, vol. 31(2), pages 65-77, June.
    14. T. Manouchehri & A. R. Nematollahi, 2019. "Periodic autoregressive models with closed skew-normal innovations," Computational Statistics, Springer, vol. 34(3), pages 1183-1213, September.
    15. Sarnaglia, A.J.Q. & Reisen, V.A. & Lévy-Leduc, C., 2010. "Robust estimation of periodic autoregressive processes in the presence of additive outliers," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2168-2183, October.
    16. Novales, Alfonso & de Fruto, Rafael Flores, 1997. "Forecasting with periodic models A comparison with time invariant coefficient models," International Journal of Forecasting, Elsevier, vol. 13(3), pages 393-405, September.
    17. Eiji Kurozumi, 2002. "Testing For Periodic Stationarity," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 243-270.
    18. Wells, J. M., 1997. "Modelling seasonal patterns and long-run trends in U.S. time series," International Journal of Forecasting, Elsevier, vol. 13(3), pages 407-420, September.
    19. Albertson, Kevin & Aylen, Jonathan, 2003. "Forecasting the behaviour of manufacturing inventory," International Journal of Forecasting, Elsevier, vol. 19(2), pages 299-311.
    20. Franses, Philip Hans & Paap, Richard, 1995. "Moving average filters and periodic integration," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(3), pages 245-249.
    21. Franses, Philip Hans & McAleer, Michael, 1997. "Testing periodically integrated autoregressive models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 43(3), pages 457-465.

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