Bayesian inference for random coefficient dynamic panel data models |
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Authors: | Fang Liu Peng Zhang Ibrahim Erkan |
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Institution: | 1. Merck &2. Co., Upper Gywnedd, PA, USA;3. XHTH Asset Management, Beijing, People's Republic of China;4. Ankara Development Agency, Ankara, Turkey |
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Abstract: | We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit's process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. We apply our approach to analyze energy and protein intakes among individuals from the Philippines. |
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Keywords: | Dynamic panel data Bayesian inference Gibbs sampling Metropolis algorithm |
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