The presence of heteroskedasticity in a considered regression model may bias the standard deviations of parameters obtained by the Ordinary Least Square (OLS) method. In this case, several hypothesis tests on the mode...The presence of heteroskedasticity in a considered regression model may bias the standard deviations of parameters obtained by the Ordinary Least Square (OLS) method. In this case, several hypothesis tests on the model under consideration may be biased, for example, CHOW’s coefficient stability test (or structural change test), Student’s t-test and Fisher’s F-test. Most of the heteroscedasticity tests in the literature are based on the comparison of variances. Despite the multiplication of equality tests of coefficients of variation (CVs) that have appeared in the literature, to our knowledge, the first and only use of the coefficient of variation in the detection of heteroskedasticity was offered by Li and Yao in 2017. Thus, this paper offers an approach to determine the existence of heteroskedasticity by a test of equality of coefficients of variation. We verify by a Monte Carlo robustness and performance test that our method seems even better than some tests in the literature. The results of this study contribute to the exploitation of the statistical measurement of CV dispersion. They help technicians economists to better verify their hypotheses before making a scientific decision when making a necessary forecast, in order to contribute effectively to the economic and sustainable development of a company or enterprise.展开更多
<p> <span style="font-family:Verdana;">To address the drawbacks of the traditional Parker test in multivariate linear</span><span style="font-family:;" "=""> ...<p> <span style="font-family:Verdana;">To address the drawbacks of the traditional Parker test in multivariate linear</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">models:</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">the process is cumbersome and computationally intensive,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">we propose a new heteroscedasticity test.</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">A new heteroskedasticity test is proposed using the fitted values of the samples as new explanatory variables, reconstructing the regression model, and giving a new heteroskedasticity test based on the significance test of the coefficients, it is also compared with the existing Parker test which is improved using the principal component idea. Numerical simulations and empirical analyses show that the improved Parker test with the fitted values of the samples proposed in this paper is superior.</span> </p>展开更多
文摘The presence of heteroskedasticity in a considered regression model may bias the standard deviations of parameters obtained by the Ordinary Least Square (OLS) method. In this case, several hypothesis tests on the model under consideration may be biased, for example, CHOW’s coefficient stability test (or structural change test), Student’s t-test and Fisher’s F-test. Most of the heteroscedasticity tests in the literature are based on the comparison of variances. Despite the multiplication of equality tests of coefficients of variation (CVs) that have appeared in the literature, to our knowledge, the first and only use of the coefficient of variation in the detection of heteroskedasticity was offered by Li and Yao in 2017. Thus, this paper offers an approach to determine the existence of heteroskedasticity by a test of equality of coefficients of variation. We verify by a Monte Carlo robustness and performance test that our method seems even better than some tests in the literature. The results of this study contribute to the exploitation of the statistical measurement of CV dispersion. They help technicians economists to better verify their hypotheses before making a scientific decision when making a necessary forecast, in order to contribute effectively to the economic and sustainable development of a company or enterprise.
文摘<p> <span style="font-family:Verdana;">To address the drawbacks of the traditional Parker test in multivariate linear</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">models:</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">the process is cumbersome and computationally intensive,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">we propose a new heteroscedasticity test.</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">A new heteroskedasticity test is proposed using the fitted values of the samples as new explanatory variables, reconstructing the regression model, and giving a new heteroskedasticity test based on the significance test of the coefficients, it is also compared with the existing Parker test which is improved using the principal component idea. Numerical simulations and empirical analyses show that the improved Parker test with the fitted values of the samples proposed in this paper is superior.</span> </p>
