A comparison of four estimators of a first order autoregressive process

• Main Author: Horn, Joseph A.
• Other Authors: Boger, Dan C.
• Language: en_US
• Published: 2012
• Online Access: http://hdl.handle.net/10945/21718

Econometricans must choose between many methods for estimating Rho in a first order autoregressive process. This thesis examines the performance of four estimators in a Monte Carlo situation. The methods examined are Durbin-Watson, Beach-MacKinnon, Theil-Nagar and Prais-Winsten. The autocorrelation coefficient, Rho, was varied from .2 to .9 and each method provided estimates of Rho and beta for 1000 replications. The results presented here are similar to those found in previous comparisons. Specifically, Ordinary Least Squares was found to be an efficient estimator of beta when autocorrelation is present only to a slight degree. Of the four estimators examined, the performance of Theil-Nagar proved superior in estimating both Rho and beta for small values of the correlation coeficient. Beach-MacKinnon, on the hand, while containing a large bias in the estimation of Rho, is the more efficient estimator of beta for large values of Rho