Bias assessment and reduction for the 2SLS estimator in general dynamic simultaneous equations models

Abstract

We consider the bias of the 2SLS estimator in general dynamic simultaneousequation models with g endogenous regressors. By using asymptotic expansion techniques we approximate 2SLS coefficient estimation bias under innovation errors, p lagged-dependent variables and strongly-exogenous explanatory variables. Large-T approximations bias of the structural form is then used to construct corrected estimators for the parameters of interest in the general DSEM (C2SLS). Simulations show that the C2SLS gives almost unbiased estimators and low mean squared errors. Alternatively, the numerical bootstrap method results suggest that the non-parametric bootstrap could be used in 2SLS for improving estimation in general DSEM.