Panel Data Models with Temporal Effects of Individual Characteristics

In most of the panel data literature, the role of individual-variant intercepts is to control for unobservable individual specific effects. The unobservables which are represented by the individual effect should have influences on the dependent variable that are constant over time but varying over individuals. The primary focus of this study is on the construction of a regression model that allows timevarying effects of individual specific components on the dependent variable. We discuss fixed effects and random effects and derive the estimators that are analogous to the within and GLS estimators of the standard panel data model. We derive the asymptotic properties of the generalized within and GLS estimators. Furthermore, we construct test statistics for the hypothesis that the individual effect has a constant coefficient over time