Estimation and Inference in Mixed Fixed and Random Coefficient Panel Data Models

In this paper, we propose to implement the EM algorithm to compute restricted maximum likelihood estimates of both the average effects and the unit-specific coefficients as well as of the variance components in a wide class of heterogeneous panel data models. Compared to existing methods, our approach leads to unbiased and more efficient estimation of the variance components of the model without running into the problem of negative definite covariance matrices typically encountered in random coefficient models. This in turn leads to more accurate estimated standard errors and hypothesis tests. Monte Carlo simulations reveal that the proposed estimator has relatively good finite sample properties. In evaluating the merits of our method, we also provide an overview of the sampling and Bayesian methods commonly used to estimate heterogeneous panel data. A novel approach to investigate heterogeneity of the sensitivity of sovereign spreads to government debt is presented.