Random‐coefficient periodic autoregressions
We propose a new periodic autoregressive model for seasonally observed time series, where the number of seasons can potentially be very large. The main novelty is that we collect the periodic parameters in a second-level stochastic model. This leads to a random-coefficient periodic autoregression with a substantial reduction in the number of parameters to be estimated. We discuss representation, estimation, and inference. An illustration for monthly growth rates of US industrial production shows the merits of the new model specification.
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Volume (Year): 65 (2011)
Issue (Month): 1 (02)
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References listed on IDEAS
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- Franses, Philip Hans, 1994. "A multivariate approach to modeling univariate seasonal time series," Journal of Econometrics, Elsevier, vol. 63(1), pages 133-151, July.
- Wolak, Frank A., 1989. "Local and Global Testing of Linear and Nonlinear Inequality Constraints in Nonlinear Econometric Models," Econometric Theory, Cambridge University Press, vol. 5(01), pages 1-35, April.
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- Helmut Herwartz, 1999. "Performance of periodic time series models in forecasting," Empirical Economics, Springer, vol. 24(2), pages 271-301.
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"Model Selection in Periodic Autoregressions,"
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Department of Economics, University of Oxford, vol. 56(4), pages 421-39, November.
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- 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.
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