Kalman filter estimation for a regression model with locally stationary errors

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.

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Year of publication:	2013
Authors:	Ferreira, Guillermo ; Rodríguez, Alejandro ; Lagos, Bernardo
Published in:	Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 62.2013, C, p. 52-69
Publisher:	Elsevier
Subject:	Estimation of the state \| Long-range dependence \| Local stationarity \| Non-stationarity \| State space system \| Time-varying models

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011056534