Performance of Alternative Predictors for the Unit Root Process
Abstract:
A comparison between Ordinary Least Squares (OLS), Weighted
Symmetric (WS), Modified Weighted Symmetric (MWS), Maximum Likelihood (ML),
and our new Modification for Least Squares (MLS) estimator for the first
order autoregressive are studied in the case of unit root using the Monte
Carlo method. The Monte Carlo study sheds some light on how well the
estimators, and the predictors on different samples size. We found that MLS
estimator is less biased and mean squares error than any other estimators,
while MWS predictor error performs well, in the sense of MSE, than any other
predictors’ methods. The sample percentiles for the distribution of the ?
statistic for the first, the second, and the third periods in the future,
for alternative estimators, are reported to know if it agrees with those of
normal distribution.
Key Words:
First order autoregressive, Unit roots estimators, and Unit roots
Author:
Ahmed H. Youssef, ahyoussef@hotmail.com
Editor:
Bradley T Ewing, bewing@ba.ttu.edu
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