Combining estimators to improve structural model estimation and inference under quadratic loss

Asymptotically, semi parametric estimators of the parameters in linear structural models have the same sampling properties. In finite samples the sampling properties of these estimators vary and large biases may result for sample sizes often found in practice. With a goal of improving asymptotic risk performance and finite sample efficiency properties, we investigate the idea of combining correlated structural equation estimators with different finite and asymptotic sampling characteristics. Based on a quadratic loss measure, we present evidence that the finite sample performance of the resulting combination estimator can be notably superior to that of a leading traditional moment based estimator.