ABSTRACT
Traditionally, statistical and econometric models have been estimated using cross-sectional or time-series data. In an increasing number of applications, however, there is availability of data based on cross sections of individuals observed over time (or other observational units such as firms, geographic entities, and so on). These data, which combine cross-sectional and time-series characteristics, are panel (or pooled) data, and allow researchers to construct and test realistic behavioral models that cannot be identified using only cross-sectional or time-series data. This chapter addresses issues in panel-data analysis such as heterogeneity, which, if not explicitly accounted for, may lead to model parameters that are inconsistent and/or meaningless. This chapter focuses on the development of panel data regression models that account for heterogeneity in a variety of ways. Furthermore, this chapter discusses issues related to the possible distortions from both the cross-sectional (heteroscedasticity) and time-series (serial correlation) dimensions to which panel data are vulnerable.
