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How to interpret heteroskedasticity in STATA? - The Student Room I found this quotation, which indicates VIF can be used for cox models. Testing for Heteroscedasticity in Stata - YouTube The Jarque-Bera test has yielded a p-value that is < 0.01 and thus it has judged them to be respectively different than 0.0 and 3.0 at a greater . Regression Diagnostics and Specification Tests - statsmodels Click on "Tests for heteroskedasticity" and press Launch to produce a second dialog box, "estat - Postestimation statistics for regress." In the box at the top,"Tests for heteroskedasticity (hettest)" should be highlighted. When heteroscedasticity is present in a regression analysis, the results of the analysis become hard to trust. You want to put your predicted values (*ZPRED) in the X box, and your residual values (*ZRESID) in the Y box. stata - Testing for heteroskedasticity in panel data vs time series ... If one or more of these assumptions are violated, then the results of our linear regression may be unreliable or even misleading. If your data passed assumption #3 (i.e., there was a linear relationship between your two variables), #4 (i.e., there were no significant outliers), assumption #5 (i.e., you had independence of observations), assumption #6 (i.e., your data showed homoscedasticity) and assumption #7 (i.e . Unusual and influential data ; Checking Normality of Residuals ; Checking Homoscedasticity of Residuals ; Checking . Specifically, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values. Homoscedasticity describes a situation in which the error term (that is, the "noise" or random disturbance in the relationship between the independent variables and the dependent variable) is the same across all values of the independent variables. According to Arellano and Bond (1991), Arellano and Bover (1995) and Blundell and Bond (1998), two . Now, click on collinearity diagnostics and hit continue. STATA Support - ULibraries Research Guides at University of Utah Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that the residuals come from a population that has homoscedasticity, which means constant variance.