Posted: 5 Dec 2010
Date Written: February 24, 2009
We consider estimation of nonlinear panel data models with common and individual specific parameters. Fixed effects estimators are known to suffer from the incidental parameters problem, which can lead to large biases in estimates of common parameters. Pooled estimators, which ignore heterogeneity across individuals, are also generally inconsistent. We assume that individuals in our data are grouped on multiple levels. These groups may be based on some external classification (for example, SIC codes), geographic location (census tract, county, state, etc.), or perhaps based on observable right hand side variables, and may be nested (hierarchical) or non-nested. We consider "group effects" estimators, where individual specific parameters are assumed common across groups at some level. We provide conditions under which group effects estimates of common parameters are asymptotically unbiased and normal. Our conditions suggest a tradeoff between two sources of bias, one due to incidental parameters and the other due to misspecification of unobserved heterogeneity. Our findings suggest that one may wish to control for heterogeneity at the group level even when individual specific effects are present. These findings are confirmed in a Monte Carlo study.
Keywords: Fixed Effects, Panel Data, Hierarchical Models
JEL Classification: C10, C13, C23
Suggested Citation: Suggested Citation
Bester, Alan and Hansen, Christian, Grouped Effects Estimators in Fixed Effects Models (February 24, 2009). Available at SSRN: https://ssrn.com/abstract=1719684