Forecasting daily time series using periodic unobserved components time series models
This discussion paper resulted in a publication in Computational Statistics & Data Analysis (2006). Vol. 51, issue 2, pages 885-903. We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend and seasonal. Periodic time series models allow dynamic characteristics to depend on the period of the year, month, week or day. In the standard multivariate approach one can interpret periodic time series modelling as a simultaneous analysis of a set of, traditionally, yearly time series where each series is related to a particular season, with a time index in years. Our analysis applies to monthly vector time series related to each day of the month. We focus on forecasting performance and the underlying periodic forecast function, defined by the in-sample observation weights for producing (multi-step) forecasts. These weights facilitate the interpretation of periodic model extensions. We take a statistical state space approach to estimate our model, so that we can identify stochastic unobserved components and we can deal with irregularly spaced time series. We extend existing algorithms to compute observation weights for forecasting based on state space models with regressor variables. Our methods are illustrated by an application to time series of clearly periodic daily Dutch tax revenues. The dimension of our model is large as we allow the time series for each day of the month to be subject to a changing seasonal pattern. Nevertheless, even with only five years of data we find that increased periodic flexibility helps help in simulated out-of-sample forecasting for two extra years of data.
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- Marius Ooms & Björn de Groot & Siem Jan Koopman, 1999.
"Time-Series Modelling of Daily Tax Revenues,"
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Oxford University Press, number 9780198523543, December.
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- Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
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"Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices,"
Tinbergen Institute Discussion Papers
03-071/4, Tinbergen Institute.
- Marius Ooms & M. Angeles Carnero & Siem Jan Koopman, 2004. "Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices," Econometric Society 2004 Australasian Meetings 158, Econometric Society.
- A. C. Harvey & Siem Jan Koopman, 2000.
"Computing Observation Weights for Signal Extraction and Filtering,"
Econometric Society World Congress 2000 Contributed Papers
0888, Econometric Society.
- Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
- Proietti Tommaso, 2004. "Seasonal Specific Structural Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-22, May.
- Osborn, Denise R., 1991. "The implications of periodically varying coefficients for seasonal time-series processes," Journal of Econometrics, Elsevier, vol. 48(3), pages 373-384, June.
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