Estimation of linear fixed-effects models with individual-specific slopes in Stata

Fixed-effects regression is considered a powerful method for estimating causal effects with survey data. However, in the linear model, the conventional technique of time-demeaning does not yield consistent estimates of the parameters when unobserved heterogeneity is not time-constant. Jeffrey M. Wooldridge (2002, Econometric Analysis of Cross Section and Panel Data [MIT Press], 317–322) derived a general model for the situation where unobserved and observed characteristics of individuals interact to produce the outcome. The fixed-effects model with individual constants and slopes (FEIS) is a remedy for coefficients that are biased due to, for example, maturation or learning where unobserved traits affect individual growth curves differently for treated and controls. The Stata xtfeis command implements the FEIS estimator in Mata, allowing for individual constants and (potentially many) slopes. Without specifying slope variables, the model collapses to the conventional model estimated by xtreg, fe that accounts for individual constants only. xtfeis implements standard errors that are robust to serial correlation or heteroskedasticity of unknown form. Estimates of the slope parameters are available optionally. The command requires panel data with at least J + 1 observations per unit, where J is the number of individual-specific slope variables (usually, but not necessarily, also including the individual-specific constant). I will present results for the effect of marriage on male wages based on real data (GSOEP and NLSY) to demonstrate the practical relevance of the method. I will use simulation results to assess robustness of the estimator to autocorrelation, measurement error, and misspecification of functional form.