What are the bad consequences of heteroskedasticity?

Recall that the two main consequences of heteroskedasticity are 1) ordinary least squares no longer produces the best estimators and 2) standard errors computed using least squares can be incorrect and misleading. Let’s first deal with the issue of incorrect standard errors.

What is heteroscedasticity What are the causes and consequences of heteroscedasticity?

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 would be then consequences for the OLS estimator if heteroscedasticity is present in a regression?

Correct! Under heteroscedasticity, provided that all of the other assumptions of the classical linear regression model are adhered to, the coefficient estimates will still be consistent and unbiased, but they will be inefficient.

Why Heteroscedasticity is a problem in regression?

Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that all residuals are drawn from a population that has a constant variance (homoscedasticity). To satisfy the regression assumptions and be able to trust the results, the residuals should have a constant variance.

What are the consequences of using OLS in the presence of autocorrelation?

The OLS estimators will be inefficient and therefore, no longer BLUE. The estimated variance of the regression coefficients will be biased and inconsistent and will be greater than the variances of estimate calculated by other methods, therefore, hypothesis testing is no longer valid.

Are errors independent in linear regression?

Assumptions for Simple Linear Regression Independence of errors: There is not a relationship between the residuals and the variable; in other words, is independent of errors. In other words, there should not look like there is a relationship.

How is Heteroscedasticity prevented?

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.

What happens if there is heteroskedasticity?

Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.

What is heteroscedasticity in regression?

What is Heteroskedasticity? Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. If there is an unequal scatter of residuals, the population used in the regression contains unequal variance, and therefore the analysis results may be invalid.

How does heteroskedasticity affect standard errors and how do we fix that?

Heteroscedasticity does not cause ordinary least squares coefficient estimates to be biased, although it can cause ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be biased, possibly above or below the true of population variance.