Estimation and Forecasting of Locally Stationary Processes |
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Authors: | Wilfredo Palma Ricardo Olea Guillermo Ferreira |
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Institution: | 1. Department of Statistics, Pontificia Universidad Católica de Chile, , Santiago, Chile;2. Department of Statistics, Universidad de Concepción, , Chile |
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Abstract: | This paper develops a state space framework for the statistical analysis of a class of locally stationary processes. The proposed Kalman filter approach provides a numerically efficient methodology for estimating and predicting locally stationary models and allows for the handling of missing values. It provides both exact and approximate maximum likelihood estimates. Furthermore, as suggested by the Monte Carlo simulations reported in this work, the performance of the proposed methodology is very good, even for relatively small sample sizes. Copyright © 2011 John Wiley & Sons, Ltd. |
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Keywords: | Kalman filter state space system nonstationarity long‐range dependence local stationarity time‐varying models |
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