Endogeneity Effect on AR (1) Models in Small Samples

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.