Essays in applied econometrics

My research examines three separate studies of applied econometrics. In the first study, I empirically assess the impact of human capital acquisition from the target firm through a merger or an acquisition on post merger CEO turnover in the acquiring firm. Little is known about the effects of a merger or an acquisition on the acquiring firm's management team. The empirical evidence shows that merger is a way to acquire talented human capital, which will change both top management team and board structure of the acquiring firm, and thus result in leadership change in the acquiring firm. Using a sample of 236 mergers during 1996 to 2000 in the US, I find: (1) 46% of CEOs of acquiring firms are replaced within 5 years; 28% leave voluntarily, and 18% are forced to step down; (2) if the target firm's top executives are retained as top executives, the acquiring firm's CEO is more likely to leave; (3) if top executives of the target firm are retained as board directors in the acquiring firm, the acquiring firm's CEO is less likely to leave voluntarily, but no change occurs in the probability of being forced out.;Next, I investigate the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including the Honore estimator and the continuously updating GMM estimator. The continuously updating GMM estimator is based on more conditional moment restrictions than the Honore estimator, and consequently is more efficient than the Honore estimator for large samples. My simulation study shows that the continuously updating GMM estimator performs not better, but in most cases worse than the Honore estimator for small samples. The reason for this finding is that the continuously updating GMM estimator is based on more moment restrictions that require discarding observations. In my design, about seventy percent of observations are discarded. The too few observations lead to an imprecise weighting matrix estimate, which in turn leads to an unreliable updating GMM estimator. This study calls for an alternative estimation method that does not rely on trimming.;In the final study, I propose a maximum likelihood estimator (MLE) for the panel data Tobit regression model with unknown individual effects. To overcome the problem occurred in chapter 2, my proposal is to use log likelihood density function instead of conditional moment restrictions in optimization problem. I suggest to approximate unknown density function of individual effects with a sieve estimator and to estimate finite dimensional unknown parameters and infinite dimensional sieve estimator jointly by applying the method of maximum likelihood estimation. Under some sufficient conditions, I show that (1) the sieve estimator of unknown density function for individual effects is consistent under certain metric; (2) the MLE estimators of the finite dimensional parameters are consistent and asymptotically normally distributed; (3) the estimator for the asymptotic covariance of the parameter is consistent.