How do you read heteroscedasticity?

One informal way of detecting heteroskedasticity is by creating a residual plot where you plot the least squares residuals against the explanatory variable or ˆy if it’s a multiple regression. If there is an evident pattern in the plot, then heteroskedasticity is present.

How do you read Eview results?

Step-By-Step Guide on Interpreting your Eviews Regression Output

1. The first line informs us that the dependent variable is GFCF (Gross Fixed Capital Formation).
2. The second line identifies the method of analysis as ordinary Least Squares.
3. The third line tells us the time and date the analysis was performed.

What is the test of heteroskedasticity?

Breusch Pagan Test It is used to test for heteroskedasticity in a linear regression model and assumes that the error terms are normally distributed. It tests whether the variance of the errors from a regression is dependent on the values of the independent variables. It is a χ2 test.

How do you fix heteroskedasticity?

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 treat heteroskedasticity?

Weighted regression The idea is to give small weights to observations associated with higher variances to shrink their squared residuals. Weighted regression minimizes the sum of the weighted squared residuals. When you use the correct weights, heteroscedasticity is replaced by homoscedasticity.

How is heteroscedasticity treated?

Is heteroscedasticity good or bad?

Heteroskedasticity has serious consequences for the OLS estimator. Although the OLS estimator remains unbiased, the estimated SE is wrong. Because of this, confidence intervals and hypotheses tests cannot be relied on. Heteroskedasticity can best be understood visually.

What does prob mean in EViews?

Prob (F-statistic): the probability value of 0.0000 is the probability value that indicates the statistical significance of the F statistic. You will prefer to have a prob-value that is less than 0.05.

What causes heteroskedasticity?

Heteroscedasticity is mainly due to the presence of outlier in the data. Outlier in Heteroscedasticity means that the observations that are either small or large with respect to the other observations are present in the sample. Heteroscedasticity is also caused due to omission of variables from the model.

What causes Heteroskedasticity?

How to perform a heteroskedasticity test in EViews?

Performing a test for Heteroskedasticity in EViews To carry out any of the heteroskedasticity tests, select View/Residual Diagnostics/Heteroskedasticity Tests. This will bring you to the following dialog: You may choose which type of test to perform by clicking on the name in the Test typebox.

How is white’s test of no heteroskedasticity computed?

White’s Heteroskedasticity Test White’s (1980) test is a test of the null hypothesis of no heteroskedasticity against heteroskedasticity of unknown, general form. The test statistic is computed by an auxiliary regression, where we regress the squared residuals on all possible (nonredundant) cross products of the regressors.

How are residuals used to test for heteroskedasticity?

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.

When is heteroscedasticity present in a regression analysis?

When heteroscedasticity is present in a regression analysis, the results of the analysis become hard to trust. Specifically, heteroscedasticity increases the variance of the regression coefficient estimates, but the regression model doesn’t pick up on this.