%0 Journal Article %A Liu, Fang %A Zhang, Peng %A Erkan, Ibrahim %A S. Small, Dylan %D 2016 %T Bayesian inference for random coefficient dynamic panel data models %U https://tandf.figshare.com/articles/journal_contribution/Bayesian_inference_for_random_coefficient_dynamic_panel_data_models/3509954 %R 10.6084/m9.figshare.3509954.v1 %2 https://tandf.figshare.com/ndownloader/files/5575706 %K Dynamic panel data %K Bayesian inference %K Gibbs sampling %K Metropolis algorithm %X

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.

%I Taylor & Francis