Panel Data Analysis

Abstract

The regression models discussed so far primarily used either cross-sectional or time series data. Each of these types of data has its exclusive features. This chapter discusses panel data regression models using the same group of entities like individuals, firms, states, countries, and the like over time. Panel data has a lot of advantages over pure cross-sectional data or pure time series data. The advantages are as follows: 1. Since panel data deals with individuals, firms, states, countries and so on over time, there is bound to be heterogeneity in these units, which may be often unobservable. The panel data estimation techniques can take such heterogeneity into account by allowing for subject-specific variables. The term subject includes micro units such individuals, firms or states. 2. By combining time series of cross-sectional observations, panel data gives “more informative data, more variability, less collinearity among variables, more degrees of freedom and more efficiency”. 3. By studying the repeated cross-sections of observations, panel data are better suited to study the dynamics of change. For example, unemployment, job turnover, duration of unemployment, and labor mobility are better studied with panel data. 4. Panel data can better detect and measure effects that cannot be observed in pure cross-sectional or time series data. For example, the effects of minimum wage laws on employment and earnings can be better studied if we follow successive waves of increases in federal and/or state minimum wages. 5. Phenomena such as economies of scale and technological change can be better studied by panel data than by pure cross-sectional or pure time series data.