How do you check heteroskedasticity in eviews?

How do you check heteroskedasticity in eviews?

To test for this form of heteroskedasticity, an auxiliary regression of the log of the original equation’s squared residuals on is performed. The LM statistic is then the explained sum of squares from the auxiliary regression divided by , the derivative of the log gamma function evaluated at 0.5.

How do you check for heteroscedasticity?

To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.

How the White’s heteroscedasticity test is used to detect heteroscedasticity?

White’s test is used to test for heteroscedastic (“differently dispersed”) errors in regression analysis. A graph showing heteroscedasticity; the White test is used to identify heteroscedastic errors in regression analysis. The null hypothesis for White’s test is that the variances for the errors are equal.

How do you solve heteroskedasticity?

How to Fix Heteroscedasticity

  1. Transform the dependent variable. One way to fix heteroscedasticity is to transform the dependent variable in some way.
  2. Redefine the dependent variable. Another way to fix heteroscedasticity is to redefine the dependent variable.
  3. Use weighted regression.

How do you fix heteroscedasticity?

Correcting for Heteroscedasticity One way to correct for heteroscedasticity is to compute the weighted least squares (WLS) estimator using an hypothesized specification for the variance. Often this specification is one of the regressors or its square.

How do you test for Multicollinearity?

One way to measure multicollinearity is the variance inflation factor (VIF), which assesses how much the variance of an estimated regression coefficient increases if your predictors are correlated. If no factors are correlated, the VIFs will all be 1.

How do you test for homogeneity?

In the test of homogeneity, we select random samples from each subgroup or population separately and collect data on a single categorical variable. The null hypothesis says that the distribution of the categorical variable is the same for each subgroup or population. Both tests use the same chi-square test statistic.

How do you solve Heteroskedasticity?

How do you fix Heteroscedasticity?