Monte Carlo simulation of ordinary least squares estimator through linear regression adaptive refined descriptive sampling algorithm

This paper shows the importance of the use of Monte Carlo experiments within Simple Linear Regression (SLR) Models through Refined Descriptive Sampling and proves practically that the asymptotic theory of the Ordinary Least Squares (OLS) estimator still hold with small samples when the Normality errors assumption is released. To this end, a simple Linear Regression adaptive Refined Descriptive Sampling (L2RDS) algorithm is proposed to estimate the parameter of SLR models by OLS method and computes its properties. Real data are used to properly specified the simulation model. The results show that L2RDS algorithm provides accurate and efficient point estimates.