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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- 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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"Model Selection in Periodic Autoregressions,"
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Department of Economics, University of Oxford, vol. 56(4), pages 421-39, November.
- Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, May.
- Helmut Herwartz, 1999. "Performance of periodic time series models in forecasting," Empirical Economics, Springer, vol. 24(2), pages 271-301.
- Franses, Philip Hans & Paap, Richard, 2004. "Periodic Time Series Models," OUP Catalogue, Oxford University Press, number 9780199242030, May.
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