A simple parameter-driven binary time series model |
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Authors: | Yang Lu |
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Affiliation: | Department of Economics (CEPN), University of Paris 13, Villetaneuse, France |
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Abstract: | We introduce a parameter-driven, state-space model for binary time series data. The model is based on a state process with a binomial-beta dynamics, which has a Markov, endogenous switching regime representation. The model allows for recursive prediction and filtering formulas with extremely low computational cost, and hence avoids the use of computational intensive simulation-based filtering algorithms. Case studies illustrate the advantage of our model over popular intensity-based observation-driven models, both in terms of fit and out-of-sample forecast. |
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Keywords: | conjugate prior state-space model switching regime |
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