Bayesian Inference for Random Coefficient Dynamic Panel Data Models
نویسنده
چکیده
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 a labor demand model for Spanish firms. JEL classification: C11;C23
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