The teaching of the classical linear regression model using Monte Carlo simulation
DOI:
https://doi.org/10.11606/issn.1982-6486.rco.2018.152100Keywords:
Monte Carlo simulation, Classical linear regression model, Sampling distribution, Least square estimatorsAbstract
This work presents a series of Monte Carlo studies using spreadsheet software aimed at facilitating the understanding of the concept of sampling distributions when students are learning the classical linear regression model. Starting from two basic spreadsheets, one for simple regression and the other for multiple regression, other spreadsheets can be easily built by introducing minor alterations in the data-generating process. The modifications that can be introduced include variations in sample size, and several characteristics of the error term, such as its variance, expected value and probability function. Different degrees of correlation between regressors can also be included. A teacher of basic econometrics can use the set of spreadsheets to obtain graphics and tables that enable the visualization of the performance of ordinary least squares estimators in different situations. Thus, students can understand in practice how violations in the underlying hypotheses of the classical linear regression model affect the performance of least square estimators, as well as the tests of hypotheses that usually accompanying the process of regression analysis. The violations analyzed in the present work include heteroscedasticity, omission of relevant variables, nonnormal errors and multicollinearity.
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