Performance of Alternative Predictors for the Unit Root Process
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
predictorsí 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
First order autoregressive, Unit roots estimators, and Unit roots
Ahmed H. Youssef, email@example.com
Bradley T Ewing, firstname.lastname@example.org
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (217959 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 2251 times since July 24, 2006.
Return to the Home Page.