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On a Family of Finite Moving-Average Trend Filters for the Ends of Series

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  • Gray, Alistair G
  • Thomson, Peter J

Abstract

A family of finite end filters is constructed using a minimum revisions criterion and based on a local dynamic model operating within the span of a given finite central filter. These end filters are equivalent to evaluating the central filter with unavailable future observations replaced by constrained optimal linear predictions. Two prediction methods are considered: best linear unbiased prediction and best linear biased prediction where the bias is time invariant. The properties of these end filters are determined. In particular, they are compared to X-11 end filters and to the case where the central filter is evaluated with unavailable future observations predicted by global ARIMA models as in X-11-ARIMA or X-12-ARIMA. Copyright © 2002 by John Wiley & Sons, Ltd.

Suggested Citation

  • Gray, Alistair G & Thomson, Peter J, 2002. "On a Family of Finite Moving-Average Trend Filters for the Ends of Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(2), pages 125-149, March.
    • Handle: RePEc:jof:jforec:v:21:y:2002:i:2:p:125-49
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    Cited by:

    1. Quenneville, Benoit & Ladiray, Dominique & Lefrancois, Bernard, 2003. "A note on Musgrave asymmetrical trend-cycle filters," International Journal of Forecasting, Elsevier, vol. 19(4), pages 727-734.
    2. Viv B Hall & Peter Thomson, 2020. "Does Hamilton’s OLS regression provide a “better alternative†to the Hodrick-Prescott filter? A New Zealand business cycle perspective," CAMA Working Papers 2020-71, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Dagum, Estela Bee, 2010. "Business Cycles and Current Economic Analysis/Los ciclos económicos y el análisis económico actual," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 28, pages 577-594, Diciembre.
    4. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.

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