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Kalman filter estimation for a regression model with locally stationary errors

Author

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  • Ferreira, Guillermo
  • Rodríguez, Alejandro
  • Lagos, Bernardo

Abstract

In this paper, a methodology for estimating a regression model with locally stationary errors is proposed. In particular, we consider models that have two features: time-varying trends and errors belonging to a class of locally stationary processes. The proposed procedure provides an efficient methodology for estimating, predicting and handling missing values for non-stationary processes.

Suggested Citation

  • Ferreira, Guillermo & Rodríguez, Alejandro & Lagos, Bernardo, 2013. "Kalman filter estimation for a regression model with locally stationary errors," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 52-69.
    • Handle: RePEc:eee:csdana:v:62:y:2013:i:c:p:52-69
    DOI: 10.1016/j.csda.2013.01.005
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    References listed on IDEAS

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    1. Wilfredo Palma & Ricardo Olea & Guillermo Ferreira, 2013. "Estimation and Forecasting of Locally Stationary Processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 86-96, January.
    2. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    3. Dahlhaus, R., 1996. "On the Kullback-Leibler information divergence of locally stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 139-168, March.
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    Cited by:

    1. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
    2. Guillermo Ferreira & Jorge Mateu & Jose A. Vilar & Joel Muñoz, 2021. "Bootstrapping regression models with locally stationary disturbances," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 341-363, June.
    3. Lujia Bai & Weichi Wu, 2021. "Detecting long-range dependence for time-varying linear models," Papers 2110.08089, arXiv.org, revised Mar 2023.

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