Specification tests in panel data models with unobserved effects and endogenous explanatory variables.

摘要

My dissertation consists of three chapters on specification testing in panel data models containing unobserved heterogeneity and endogenous variables. The first chapter, "Regression Based Specification Testing for Panel Data Models Estimated by Fixed Effects 2SLS," derives specification tests for linear panel data models estimated by fixed effects or fixed effects instrumental variables methods. I propose convenient, regression-based tests for endogeneity, over-indentification, and non-linearities. Importantly, some versions of the tests are robust to heteroskedasticity and serial correlation. As a illustration, I test for non-linearities and for endogenous spending in analyzing the affect of spending on student performance.; The second chapter, "Testing for Correlated Random Effects in Panel Data Models Estimated using Instrumental Variables," proposes variable addition tests for correlation between the unobserved heterogeneity and the instrumental variables. In addition to the standard model with a single additive effect, I consider the extension to models with unit-specific trends. As an illustration, I test for correlated district effects and trending district effects using panel data on student performance and educational spending.; The third chapter, 'Testing the Conditional Variance and Unconditional Variance in Panel Data Models Estimated by Fixed Effects 2SLS," develops a robust regression based test for heteroskedasticity. In addition, I derive a test for the unconditional variance. I apply the test for heteroskedasticity to a panel data model that explains student performance in terms of spending, poverty rates, and enrollment.