Intrinsic Bayesian estimation of linear time series models
Intrinsic loss functions (such as the Kullback–Leibler divergence, i.e. the entropy loss) have been used extensively in place of conventional loss functions for independent samples. But applications in serially correlated samples are scant. In the present study, we examine Bayes estimator of Linear...
| Main Authors: | , |
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| 格式: | 文件 |
| 语言: | English |
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Taylor & Francis Group
2021-10-01
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| 丛编: | Statistical Theory and Related Fields |
| 主题: | |
| 在线阅读: | http://dx.doi.org/10.1080/24754269.2020.1744073 |
| 总结: | Intrinsic loss functions (such as the Kullback–Leibler divergence, i.e. the entropy loss) have been used extensively in place of conventional loss functions for independent samples. But applications in serially correlated samples are scant. In the present study, we examine Bayes estimator of Linear Time Series (LTS) model under the entropy loss. We derive the Bayes estimator and show that it involves a frequentist expectation of regressors. We propose a Markov Chain Monte Carlo procedure that jointly simulates the posteriors of the LTS parameters with frequentist expectation of regressors. We conduct Bayesian estimation of an LTS model for seasonal effects in some U.S. macroeconomic variables. |
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| ISSN: | 2475-4269 2475-4277 |