Consistent Estimation, Model Selection And Averaging Of Dynamic Panel Data Models With Fixed Effect

In the context of an autoregressive panel data model with fixed effect, the authors examine the relationship between consistent parameter estimation and consistent model selection. Consistency in parameter estimation is achieved by using the transformation of the fixed effect proposed by Lancaster (2002). They find that such transformation does not necessarily lead to consistent estimation of the autoregressive coefficient when the wrong set of exogenous regressors are included. To estimate the model consistently and to measure its goodness of fit, they argue for comparing different model specifications using the Bayes factor rather than the Bayesian information criterion based on the biased maximum likelihood estimates. When the model uncertainty is substantial, they recommend the use of Bayesian Model Averaging.