Dynamic Panel Data Models: Theory and Applications

Panel data has important advantages over purely cross-sectional or time-series data in studying many social and economic problems, because it contains information about both the intertemporal dynamics and the individuality of the entities being investigated. Dynamic panel data models are a commonly used class of models for panel studies which identify the parameters of interest through an overdetermined system of estimating equations. Two important problems that arise in such models are the following: (1) It may not be clear priori whether certain estimating equations are valid. (2) Some of the estimating equations may only "weakly" identify the parameters of interest, providing little information about these parameters and making inference based on conventional asymptotic theory misleading. A procedure based on empirical likelihood for choosing among possible estimators and selecting variables in this setting is developed. The advantages of the procedure over other approaches in the econometric literature are demonstrated through theoretical analysis and simulation studies. Applications of dynamic panel data models to panel studies of the impact of agricultural commercialization on nutrition in developing countries and to panel studies of the determinants of educational achievement are described. Methodological issues related to the analysis and design of these studies are discussed.