A NONSTATIONARY TIME SERIES MODEL AND ITS FITTING BY A RECURSIVE FILTER |
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| Authors: | Genshiro Kitagawa |
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| Affiliation: | The Institute of Statistical Mathematics, 4–6–7 Minami-Azabu, Minato-ku, Tokyo 106, Japan and The University of Tulsa, 600 South College Avenue, Tulsa, Oklahoma 74104, U.S.A. |
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| Abstract: | Abstract. The use of the state space representation for the analysis of nonstationary time series is proposed. For the fitting of the models, the use of a modified AIC based on the likelihood of the innovation process is proposed. A square root filter/smoother algorithm for the evaluation of the likelihood and state estimation is discussed. |
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| Keywords: | Nonstationary time series Seasonal adjustment Trend estimation AIC Kalman filter Square root filter/smoother |
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