A transitional Markov switching autoregressive model |
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Authors: | J Cheng |
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Institution: | 1. Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, ChinaJie.cheng@xjtlu.edu.cn |
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Abstract: | ABSTRACTThis paper is concerned with properties of a transitional Markov switching autoregressive (TMSAR) model, together with its maximum-likelihood estimation and inference. We extend existing MSAR models by allowing dependence of AR parameters on hidden states at time points prior to the current time t. A stationary solution is given and expressions for the theoretical autocovariance function are derived. Two time series are analyzed and the new model outperforms two existing MSAR models in terms of maximized log-likelihood, residual correlations, and one-step-ahead forecasting performance. The new model also gives more regime changes in agreement with real events. |
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Keywords: | Autocovariance structure Filter and smoothed probabilities Markov switching autoregressive models Stationary time series |
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