In the first chapter, we examine the relation between efficiency and competition in a dynamic framework. For this purpose, we measure efficiencies and conduct of the U.S. airlines in two city-pairs. We model the conduct parameter as an unobservable state variable which is an AR(1) process and estimate it by the square-root Kalman filter technique. Our results accords with Hick's (1935) 'quiet life hypothesis.'

In the second chapter, we propose using the Kalman filter estimator (KFE) to estimate the technical efficiency. We assume that the effects term is an AR(1) process or a random walk. We apply the KFE to estimate the average efficiencies of the U.S. airlines during the period 1971-1986. We found evidence of 'quiet life hypothesis.'

In the third chapter, we estimate the time-varying efficiencies of the U.S. banks during 1984-1995 with four different efficiency estimators. Using these series of efficiency estimates, we make a multivariate Kalman filter analysis to examine the efficiency trend in the G.S. banks during this period. We observed that the regulations and innovations had a positive effect on the efficiency of G.S. banks as expected. However, this positive effect decayed through time.

The fourth chapter generalizes the well-known Battese-Coelli (1992) (BC) estimator to allow endogenous regressors. The regressors are still assumed to be independent of the effects though they are correlated with the irregular term. The simulations show the superiority of our method to the BC estimator.

Efficiency is a residual that is caused by managerial mistakes. In the final chapter, we propose some strategies to minimize such mistakes in the Stackelberg competition world with price discrimination. We suggest that the leader should use its preemptive advantage to attract the highest value customers and that the follower should price discriminate over the residual demand.