Policy Analysis Using Multilevel Regression Models with Group Interactive Fixed Effects

The use of multilevel regression models is prevalent in policy analysis to estimate the effect of group level policies on individual outcomes. In order to allow for the time varying effect of group heterogeneity and the group specific impact of time effects, we propose a group interactive fixed effects approach that employs interaction terms of group factor loadings and common factors in this model. For this approach, we consider the least squares estimator and associated inference procedure. We examine their properties under the large <i>n</i> and fixed <i>T</i> asymptotics. The number of groups, <i>G</i>, is allowed to grow but at a slower rate. We also propose a test for the level of grouping to specify group factor loadings, and a GMM approach to address policy endogeneity with respect to idiosyncratic errors. Finally, we provide empirical illustrations of the proposed approach using two empirical examples.