Evaluation of simultaneous equation techniques in the presence of misspecification error: a Monte Carlo approach

Abstract

One of the assumptions of Classical Linear Regression Model (CLRMA), is that the regression model be ‘correctly’ specified. If the model is not ‘correctly’ specified, the problem of model misspecification error arises. The objective of the study is to know the performances of the estimator and also the estimator that is greatly affected by misspecification error due to omission of relevant explanatory variable. Four simultaneous equation techniques (OLS, 2SLS, 3SLS, LIML) were applied to a two-equation model and investigated on their performances when plagued with the problem of misspecification error. A Monte Carlo method simulation method was employed to investigate the effect of these estimators due to misspecification of the model. The findings revealed that the estimates obtained by 2SLS and 3SLS are similar and variances by all the estimates reduced consistently as the sample size increases. The study had revealed that 2 3 SLS performed best using average of parameter criterion while OLS generated the least variances. LIML is mostly affected by misspecification