Implementation of the heteroskedasticity testing for linear regression model
L Malyarets, E Kovaleva, I Lebedeva, E Misyura, O Dorokhov
COMPUTER MODELLING & NEW TECHNOLOGIES 2017 21(3) 56-63
Simon Kuznets Kharkiv National University of Economics, Nauky Avenue, 9-A, Kharkiv, Ukraine, 61166
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. The advantage of this paper is that real empirical data are used to test for heteroskedasticity. For implementing the testing there is developed the special software with using of the algorithmic programming language MATLAB. The purpose of the article is to describe the functions implemented in the form of m-files (MATLAB environment files) to check for heteroskedasticity in multifactor regression models. To do this, modified algorithms for the tests on heteroskedasticity were used. Experimental studies of the work of the program were carried out for various linear regression models both the 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.