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A Nonstationary Time Series Model And Its Fitting By A Recursive Filter

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  • Genshiro Kitagawa

Abstract

. The use of the state space representation for the analysis of nonstationary time series is proposed. For the fitting of the models, the use of a modified AIC based on the likelihood of the innovation process is proposed. A square root filter/smoother algorithm for the evaluation of the likelihood and state estimation is discussed.

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  • Genshiro Kitagawa, 1981. "A Nonstationary Time Series Model And Its Fitting By A Recursive Filter," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(2), pages 103-116, March.
    • Handle: RePEc:bla:jtsera:v:2:y:1981:i:2:p:103-116
    DOI: 10.1111/j.1467-9892.1981.tb00316.x
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    Cited by:

    1. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    2. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.
    3. Víctor Gómez & Félix Aparicio‐Pérez, 2009. "A new state–space methodology to disaggregate multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 97-124, January.
    4. Fushing Hsieh & Emilio Ferrer & Shu-Chun Chen & Sy-Miin Chow, 2010. "Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 351-372, June.
    5. Philip Hans Franses & Yoshinori Kawasaki, 2004. "Do seasonal unit roots matter for forecasting monthly industrial production?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 77-88.
    6. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    7. H. Visser & A. Petersen, 2009. "The likelihood of holding outdoor skating marathons in the Netherlands as a policy-relevant indicator of climate change," Climatic Change, Springer, vol. 93(1), pages 39-54, March.
    8. Schlicht, Ekkehart & Pauly, Ralf, 1982. "Descriptive Seasonal Adjustment by Minimizing Perturbations," Darmstadt Discussion Papers in Economics 16, Darmstadt University of Technology, Department of Law and Economics.
    9. Fackler, Paul L., 1989. "Modeling Trend and Higher Moment Properties of U.S. Corn Yields," 1989 Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk Meeting, April 9-12, 1989, Sanibel Island, Florida 271523, Regional Research Projects > S-232: Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk.
    10. T. Higuchi, 1991. "Frequency domain characteristics of linear operator to decompose a time series into the multi-components," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(3), pages 469-492, September.
    11. Chen, Chunhang, 1997. "Robustness properties of some forecasting methods for seasonal time series: A Monte Carlo study," International Journal of Forecasting, Elsevier, vol. 13(2), pages 269-280, June.
    12. Koki Kyo & Hideo Noda & Genshiro Kitagawa, 2022. "Co-movement of Cyclical Components Approach to Construct a Coincident Index of Business Cycles," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 101-127, March.
    13. Yoshinori Kawasaki & Philip Hans Franses, 2003. "Detecting seasonal unit roots in a structural time series model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(4), pages 373-387.
    14. Antonio García Ferrer & Juan del Hoyo Bernat & Peter C. Young & Alfonso Novales Cinca, 1993. "Recursive identification, estimation and forecasting of nonstationary economic time series with applications to GNP international data," Documentos de Trabajo del ICAE 9310, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.

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