A Monte Carlo Study of the Regression Model with Autocorrelated Disturbances
Description of the relative performance of estimators based on the results of a Monte Carlo experiment, under the assumption that disturbances are generated by a first-order autoregressive process. To generate artificial data for the experiment, eight structures were specified: samples of size 30 were drawn for four structures; samples of size 100 for the other four. For each structure, 300 samples were drawn and estimates of unknown parameters were calculated for each sample by five different methods, namely, maximum likelihood, Theil-Nager, approximate Bayes, Durbin, and least squares estimators. The task was first to examine the performance of the various estimators and second, to check the behavior of several commonly used tests of independence regression analysis. Characteristics of the various structures were chosen to represent a variety of circumstances that might be reasonably encountered in practical work.