Three Essays On Panel Data Estimation

Three Essays on Dynamic Panel Data Estimation

This dissertation consists of three essays, first two of which consider a new estimation method of dynamic panel data models and the last one considers an application of these models. The first essay (Chapter 1) offers empirical likelihood (EL) estimation of dynamic panel data models, which provide great flexibility to empirical researchers. EL estimation method is shown to have great advantages in usual settings, however little is known on the relative merits of these estimators in panel data models. With this essay, we try to fill that gap by establishing the asymptotic properties of the EL estimator for a dynamic panel model with individual effects when both the time and the cross-section dimensions tend to infinity. We give the conditions under which this estimator is consistent and asymptotically normal. In the second essay (Chapter 2), via a Monte Carlo study, we assess the relative finite sample performances of EL, generalized method of moments, and limited information maximum likelihood estimators for an autoregressive panel data model when there are many moment conditions. We also extend our results to the many weak moments settings. Our results suggest that when the overall performances are concerned, in terms of median, interquartile range and median absolute error of the estimators, in both strong and weak moments settings, EL is more reliable. In the final essay (Chapter 3) we consider an application of dynamic panel data models to examine the determinants of the allocation of state highway funds using panel data for North Carolina's 100 counties for the years 1990 to 2005. We make two main contributions with this essay. First, although there have been numerous studies of highway funding at the state level, to our knowledge, there is no analysis at the sub-state or county levels. Second, by using dynamic panel data models and sophisticated methods to estimate them, we account for any potential persistence in the process of adjustment toward an equilibri.

Three Essays in Econometrics

Three Essays on Panel Data Estimation

This work discusses various aspects of panel data estimation. In chapter one, an algorithm for semiparametric random effects estimation is proposed. The performance of bootstrap-based confidence intervals for the proposed estimators are examined and found reasonable. The algorithm is also applied to a set of U.S. state level medical expenditure data to estimate the medical Engel curve. In the second chapter, the predictive performance of various parametric and semiparametric panel data estimators is compared on the same dataset of U.S. state level medical expenditures as well as out of sample forecast performance and bootstrap bias-corrected mean square errors of the competing estimators are evaluated. In general, the estimator discussed in the first chapter is found to perform well. In the third chapter a generalized method of moments estimator is investigated under various norms.