A Comparative Study of the Classical and Bayesian Methods of Estimating a Just-Identified Simultaneous Equations Econometric Model

D.K Shangodoyin,D M Okewole,O E Olubusoye

doi:10.5539/ass.v7n5p222

Abstract

A just identified two-equation econometric model is simulated using both Classical and Bayesian procedures. The estimates of the parameters for both methods were compared under a wide range of scenarios; sample size, residual variance and variance of the data on the predetermined variable. The Monte Carlo experiment was performed using E-veiws and WinBUGS computer softwares. The median, being a robust estimator of average in terms of validity, was used as the posterior estimate. As indicated in similar research in the past where the posterior mode was used as estimate, the Bayesian procedure performed better in most cases, while some scenarios showed similar behavior for the two procedures.

Highlights

Simultaneous equations model (SEM) is a very important field of Econometrics
The various estimators were not separately considered because they give the same estimate for this model being a just-identified model
We noticed that in run I and II, the mean squared error was questionably large for the classical method when N=20, this is as a result of outliers that are uncharacteristic of the Bayesian method

Summary

Introduction

Simultaneous equations model (SEM) is a very important field of Econometrics. Some important Statistical implications of a linear simultaneous equation model were presented by Haavelmo (1943), such as estimation of the stochastic equations which should not be done separately. The indirect least squares method, two-stage least squares method, k-class estimators, three-stage least squares method, full information maximum likelihood method, Jackknife instrumental variable method due to Angrist, Imbens and Krueger (1999) and Blomquist and Dahlberg (1999) method are the well known classical inferential approaches that have been in use. They are majorly extensions of the two basic techniques of single-equation methods, the ordinary least squares and maximum likelihood. A comparative study of the classical and the Bayesian approaches is necessary so as to take advantage of their strength and research more on possible ways of improving on their weaknesses

Methods

Results

Conclusion