The Heteroskedasticity Tests Implementation for Linear Regression Model Using MATLAB

The article discusses the problem of heteroskedasticity, which can arise in the process of calculating econometric models of large dimension and ways to overcome it. Heteroskedasticity distorts the value of the true standard deviation of the prediction errors. This can be accompanied by both an increase and a decrease in the confidence interval. We gave the principles of implementing the most common tests that are used to detect heteroskedasticity in constructing linear regression models, and compared their sensitivity. One of the achievements of this paper is that real empirical data are used to test for heteroskedasticity. The aim of the article is to propose a MATLAB implementation of many tests used for checking the heteroskedasticity in multifactor regression models. To this purpose we modified few open algorithms of the implementation of known tests on heteroskedasticity-. Experimental studies for validation the proposed programs were carried out for various linear regression models. The models used for comparison are models of the Department of Higher Mathematics and Mathematical Methods in Economy of Simon Kuznets Kharkiv National University of Economics and econometric models which were published recently by leading journals.