Bias-corrected Estimation in Dynamic Panel Data Models with Heteroscedasticity

This study extends earlier results on bias-corrected estimators for the fixed-effects dynamic panel data model. We derive the inconsistency of the LSDV estimator for finite T and N large in case of both time-series and cross-section heteroscedasticity and show how to implement it in bias correction procedures. Keywords: Bias correction; Dynamic panel data model; Heteroscedasticity; Least squares dummy variable estimator JEL classification codes: C13; C2

A Comparison of Bias Approximations for the Two-Stage Least Squares (2SLS) Estimator

We consider the bias of the two-stage least squares (2SLS) estimator in linear instrumental variable regression with only one endogenous regressor. By using asymptotic expansion techniques, we approximate the 2SLS coefficient estimation bias under various scenarios regarding the number and strength of instruments. Highlights ► The 2SLS coefficient estimation bias is approximated. ► We consider various scenarios regarding the number and strength of instruments. ► The resulting approximation encompasses existing bias expressions. ► Simulations show that the developed approximation is accurate

Crime, deterrence and punishment revisited

Despite an abundance of empirical evidence on crime spanning over 40 years, there exists no consensus on the impact of the criminal justice system on crime activity. We construct a new panel data set that contains all relevant variables prescribed by economic theory. Our identification strategy allows for a feedback relationship between crime and deterrence variables, and it controls for omitted variables and measurement error. We deviate from the majority of the literature in that we specify a dynamic model, which captures the essential feature of habit formation and persistence in aggregate behaviour. Our results show that the criminal justice system exerts a large influence on crime activity. Increasing the risk of apprehension and conviction is more influential in reducing crime than raising the expected severity of punishment