This study employs Monte Carlo experiments to evaluate the performances of a number of common panel data estimators when serial correlation and cross-sectional dependence are both present. It focuses on fixed effects models with less than 100 cross-sectional units and between 10 and 25 time...

Our study revisits Beck and Katz' (1995) comparison of the Parks and PCSE estimators using time-series, cross-sectional data (TSCS). Our innovation is that we construct simulated statistical environments that are designed to approximate actual TSCS data. We pattern our statistical environments...

This study investigates U.S. state economic growth from 1970-1999. I innovate on previous studies by developing a new approach for addressing "model uncertainty" issues associated with estimating growth equations. My approach borrows from the "extreme bounds analysis" (EBA) approach of Leamer...

I estimate the relationship between taxes and economic growth using data from 1970-1999 and the forty-eight continental U.S. states. I find that taxes used to fund general expenditures are associated with significant, negative effects on economic growth. Further, this finding is robust across...

This research presents a simple procedure for improving state-specific estimates of marginal tax rates (MTR’s). Most research employing MTR’s follows a procedure developed by Koester and Kormendi (K&K, 1987). Unfortunately, the time-invariant nature of the K&K estimates precludes...

Panel data characterized by groupwise heteroscedasticity, cross-sectional correlation, and AR(1) serial correlation pose problems for econometric analyses. It is well known that the asymptotically efficient, FGLS estimator (Parks) sometimes performs poorly in finite samples. In a widely cited...

Our study empirically investigates the relationship between constructed-response (CR) and multiple-choice (MC) questions using a unique data set compiled from several years of university introductory economics classes. We conclude that CR and MC questions do not measure the same thing. Our main...