Three essays on the statistical inference of dynamic panel models

2015-06-18Dynamic panel models has very wide economic application in labor economics, health economics as well as development economics. However, there are unique features for dynamic panel models: (i) the presence of time-invariant individual specific effects raises the issue of incidental parameters, be the specific effects are considered random or fixed; (ii) the formulation of initial observations; and (iii) the multi-dimensional nature of panel data. For the first one, certain transformation has to be used to remove the individual effects. After transformation, method of moments estimation can be applied to estimate the lag coefficient. However, it is shown in the literature that the generalized method of moments (GMM) estimator is asymptotically biased. Since the reliability of statistical inference depends critically on whether the estimator is asymptotically unbiased or not, if we want to make valid inference, then we need to obtain unbiased estimator in the first hand. In this article, we propose two approaches which will lead to unbiased estimator for dynamic panel models. The first approach is the maximum likelihood estimation (MLE) for dynamic panel models with serial uncorrelated errors, and the second approach is the jackknife instrumental variables estimation (JIVE) for dynamic panel models with serial correlated errors. It is shown in this article that both the MLE and JIVE estimators are asymptotically unbiased and asymptotically normally distributed. Monte Carlo simulations are conducted to examine the finite sample properties of MLE and JIVE, and simulation findings confirm the our findings in this article