Assessment of simultaneous equation techniques under the influence of outliers

Abstract

Most simultaneous equations estimation techniques are based on the assumptions of normality which gives little consideration to some atypical data often called outliers which may be present in the observations. The outliers may have some obvious distorting influence on the estimates produce by these techniques. This study investigates the distorting effect of outliers on four simultaneous equation estimation techniques through Monte Carlo method. Outliers of various degrees were introduced into observations of different sizes. The estimators were ranked based on their ability to absorb the shock due to outliers in the observations. The Total Absolute Bias (TAB), Variance and Root Mean Square Error (RMSE) were used in ranking the performances of the estimators. Based on the criterion of tab, two stages least squares (2SLS) ranked the best, closely followed by three stage least squares (3SLS) and ordinary least squares (OLS) in that order, while limited information maximum likelihood (LIML) was the poorest when outliers of not more than 5% are present in the observation. It is however, not strange to observe that OLS outperformed the other estimators when variance was used. This could be misleading since variance may be measured around a wrong parameter. Based on the criterion of RMSE, ordinary least squares yields estimates with the least value of RMSE while LIML yields the greatest when outliers of not more than 10% are present in the observation. Also it was established that OLS has the greatest capacity to absorb the shock due to the presence of outliers in the observation