Panel analysis

Panel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics to analyze two-dimensional (typically cross sectional and longitudinal) panel data. The data are usually collected over time and over the same individuals and then a regression is run over these two dimensions. Multidimensional analysis is an econometric method in which data are collected over more than two dimensions (typically, time, individuals, and some third dimension). Panel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics to analyze two-dimensional (typically cross sectional and longitudinal) panel data. The data are usually collected over time and over the same individuals and then a regression is run over these two dimensions. Multidimensional analysis is an econometric method in which data are collected over more than two dimensions (typically, time, individuals, and some third dimension). A common panel data regression model looks like y i t = a + b x i t + ε i t {displaystyle y_{it}=a+bx_{it}+varepsilon _{it}} , where y is the dependent variable, x is the independent variable, a and b are coefficients, i and t are indices for individuals and time. The error ε i t {displaystyle varepsilon _{it}} is very important in this analysis. Assumptions about the error term determine whether we speak of fixed effects or random effects. In a fixed effects model, ε i t {displaystyle varepsilon _{it}} is assumed to vary non-stochastically over i {displaystyle i} or t {displaystyle t} making the fixed effects model analogous to a dummy variable model in one dimension. In a random effects model, ε i t {displaystyle varepsilon _{it}} is assumed to vary stochastically over i {displaystyle i} or t {displaystyle t} requiring special treatment of the error variance matrix.