Improved GMM Estimation of AR(1) Time Series with a Root near 1

B.F. Chakalabbi¹ , Sanmati. Neregal² , Sagar. Matur³

1. Department of Statistics, Karnatak Arts College (Karnatak University), Dharwad, India.
2. Department of Statistics, Karnatak Arts College (Karnatak University), Dharwad, India.
3. Department of Statistics, Karnatak Arts College (Karnatak University), Dharwad, India.

Correspondence should be addressed to: bfckcd@gmail.com.

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.1 , pp.1-10, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v5i1.110

Online published on Feb 28, 2018

Copyright © B.F. Chakalabbi , Sanmati. Neregal , Sagar. Matur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In this paper, to estimate AR(1) time series model First-difference GMM and Level GMM estimation methods have been considered, which have already performed well for estimation of AR(1) panel data model. A Monte Carlo simulation is carried out in order to study the performances of the above mentioned estimators and OLS estimator. Further, comparison among these estimators have been done in terms of bias and RMSE. Study reveals that, in many cases the OLS and First difference GMM estimators behave same in terms of Bias and RMSE. For all the negative values of autoregressive parameter the RMSE and bias of Level GMM estimator is larger than the remaining estimators. But in the case of positive values of autoregressive parameter Level GMM estimator performs better than First-difference GMM and OLS estimators especially, when sample size is small and autoregressive parameter is close to one.

Key-Words / Index Term :
AR(1), First-difference GMM estimation, Level GMM estimation, OLS, Monte Carlo simulation

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