Abstract
M.Com.(Econometrics)
The objective of this study is to evaluate different estimation techniques that can be used to estimate the coefficients of a model. The estimation techniques were applied to empirical data drawn from the South African economy. The Monte Carlo studies are unique in that data was statistically generated for the experiments. This approach was due to the fact that actual observations on economic variables contain several
econometric problems, such as autocorrelation and MUlticollinearity, simultaneously. However, the approach in this study differs in that empirical data is used to evaluate the
estimation techniques.
The estimation techniques evaluated are :
• Ordinary least squares method
• Two stage least squares method
• Limited information maximum likelihood method
• Three stage least squares method
• Full information maximum likelihood method.
The estimates of the different coefficients are evaluated on the
following criteria :
• The bias of the estimates
• The variance of the estimates
• t-values of the estimates
• The root mean square error.
The ranking of the estimation techniques on the bias criterion
is as follows :
1 Full information maximum likelihood method.
2 Ordinary least squares method
3 Three stage least squares method
4 Two stage least squares method
5 Limited information maximum likelihood method
The ranking of the estimation techniques on the variance criterion is as follows :
1 Full information maximum likelihood method.
2 Ordinary least squares method
3 Three stage least squares method
4 Two stage least squares method
5 Limited information maximum.likelihood method
All the estimation techniques performed poorly with regard to the statistical significance of the estimates. The ranking of the estimation techniques on the t-values of the estimates is thus as follows
1 Three stage least squares method
2 ordinary least squares method
3 Two stage least squares method and the limited information maximum likelihood method
4 Full information maximum likelihood method.
The ranking of the estimation techniques on the root mean square error criterion is as follows :
1 Full information maximum likelihood method and the ordinary least squares method
2 Two stage least squares method
3 Limited information maximum likelihood method and the three stage least squares method
The results achieved in this study are very similar to those of the Monte Carlo studies. The only exception is the ordinary least squares method that performed better on every criteria dealt with in this study. Though the full information maximum likelihood method performed the best on two of the criteria, its performance was extremely poor on the t-value criterion.
The ordinary least squares method is shown, in this study, to be the most constant performer.