Comparative Study Of Estimators In Autocorrelated-Endogenized Linear Model
by Olutunji John Olaomi, Dahud Kehinde Shangodoyin.
This study compares the estimators of linear model when the least square
assumptions of independence of the error terms and the zero correlation between the
regressor and the error terms are violated using a Monte Carlo experiment. OLS, OLSA, 2SLS
and 2SLSA estimators were considered in 10,000 replications of the experiment on single
equation model where the error terms are AR(1) autocorrelated and at the same time
significantly correlated with the regressor(endogeneity). We consider autocorrelation
levels 0.4, 0.8 and 0.9, endogeneity levels between the regressor and the error terms at
0.01, 0.02 and 0.05 each at the sample size (N) 20, 40 and 60. The estimators are adjudged
using the RMSE criterion on the 108 scenarios. The result shows that 2SLS perform best when
N<=40 at all autocorrelation and endogeneity levels. OLSA is the best estimator when N is
large (N = 60) and autocorrelation level is <= 0.8, while 2SLSA performs best when N and
autocorrelation level are large, at all endogeneity levels. All estimators perform worse as
autocorrelation level increases while they perform better asymptotically and with increase
in significant level.
Monte-Carlo Experiment, Endogeneity, 2SLS, OLS, Autocorrelation
Olutunji John Olaomi, email@example.com
Dahud Kehinde Shangodoyin, firstname.lastname@example.org
Sapra, Sunil K., email@example.com.
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