Kalman filter estimation for a regression model with locally stationary errors

Guillermo Ferreira, Alejandro Rodríguez and Bernardo Lagos

Computational Statistics & Data Analysis, 2013, vol. 62, issue C, 52-69

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.

Keywords: Estimation of the state; Long-range dependence; Local stationarity; Non-stationarity; State space system; Time-varying models (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:62:y:2013:i:c:p:52-69

DOI: 10.1016/j.csda.2013.01.005

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