TITLE:
Endogeneity Effect on AR (1) Models in Small Samples
AUTHORS:
Yakubu Dekongmene Kanyir, John O. Olaomi, Albert Luguterah
KEYWORDS:
Autoregressive Linear Model, Endogeneity, OLS, 2SL, GMM
JOURNAL NAME:
Modern Economy,
Vol.13 No.9,
September
21,
2022
ABSTRACT: This study examines the endogeneity effect on
autoregressive linear models of AR (1) in small samples, making use of the
Ordinary Least Square (OLS) estimator, Two-Stage Least Squares (2SLS)
estimator, and Generalized Method of Moment (GMM) estimator, based on the
sensitivity analysis of sample size and specification errors in estimator
determination in linear regression model through the use of Monte Carlo
simulation and application to real-life data. The simulation indicates that
2SLS and GMM estimators show the smallest biases when the sample size is varied
from n = 10, 25, 50 to 100. The
estimator that performs best when sample size n = 10 across autocorrelation (ρ)
and significant correlation (α) at
all levels of replication of 10,000 is GMM. In the real-life data, OLS and 2SLS
exhibit higher endogeneity characteristics from the dataset used. The empirical
analysis base on MSE criteria GMM is the best estimator for dealing with
external shock factors to inflations embedded with endogeneity in the linear model.
When endogeneity and autocorrelation are bedeviled in a linear AR (1) model, in
small samples, using the GMM estimator will provide the best results in small
samples than using 2SLS and OLS.