Bayesian inference for random coefficient dynamic panel data models

Fang Liu, Peng Zhang, Ibrahim Erkan and Dylan S. Small

Journal of Applied Statistics, 2017, vol. 44, issue 9, 1543-1559

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:44:y:2017:i:9:p:1543-1559

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DOI: 10.1080/02664763.2016.1214248

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