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Computing the mean square error of unobserved components extracted by misspecified time series models

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  • Harvey, Andrew C.
  • Delle Monache, Davide

Abstract

Algorithms are presented for computing mean square errors in a misspecified unobserved components model when the true model is known. It is assumed that both the true and misspecified models can be put in linear state space form. The algorithm for filtering is based on the Kalman filter while that for smoothing modifies the fixed-point smoother. Illustrations include the efficiency of the Hodrick-Prescott filter for annual flow data and the mean square error of predictions for misspecified models from the autoregressive integrated moving average class.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 33 (2009)
Issue (Month): 2 (February)
Pages: 283-295

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Handle: RePEc:eee:dyncon:v:33:y:2009:i:2:p:283-295

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Web page: http://www.elsevier.com/locate/jedc

Related research

Keywords: Detrending Exponentially weighted moving average Hodrick-Prescott filter Kalman filter Smoother;

References

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  1. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
  2. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
  3. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
  4. Maravall, Agustin, 1985. "On Structural Time Series Models and the Characterization of Components," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 350-55, October.
  5. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
  6. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
  7. Tommaso Proietti, 2005. "Forecasting and signal extraction with misspecified models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 539-556.
  8. Gomez, Victor, 2001. "The Use of Butterworth Filters for Trend and Cycle Estimation in Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 365-73, July.
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Citations

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Cited by:
  1. Alejandro Rodríguez & Esther Ruiz, 2010. "Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters," Statistics and Econometrics Working Papers ws100301, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. Theofilakou, Nancy & Stournaras, Yannis, 2012. "Current account adjustments in OECD countries revisited: The role of the fiscal stance," Journal of Policy Modeling, Elsevier, vol. 34(5), pages 719-734.
  3. Gebhard Flaig, 2012. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," CESifo Working Paper Series 3816, CESifo Group Munich.

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