# Question: Is Heteroscedasticity Good Or Bad?

## What are the consequences of Heteroscedasticity?

Consequences of Heteroscedasticity The OLS estimators and regression predictions based on them remains unbiased and consistent.

The OLS estimators are no longer the BLUE (Best Linear Unbiased Estimators) because they are no longer efficient, so the regression predictions will be inefficient too..

## What does Heteroscedasticity mean?

In statistics, heteroskedasticity (or heteroscedasticity) happens when the standard deviations of a predicted variable, monitored over different values of an independent variable or as related to prior time periods, are non-constant. … Heteroskedasticity often arises in two forms: conditional and unconditional.

## What happens when Homoscedasticity is violated?

Violation of the homoscedasticity assumption results in heteroscedasticity when values of the dependent variable seem to increase or decrease as a function of the independent variables. Typically, homoscedasticity violations occur when one or more of the variables under investigation are not normally distributed.

## Can R Squared be more than 1?

The Wikipedia page on R2 says R2 can take on a value greater than 1.

## What is perfect Multicollinearity?

Perfect multicollinearity is the violation of Assumption 6 (no explanatory variable is a perfect linear function of any other explanatory variables). Perfect (or Exact) Multicollinearity. If two or more independent variables have an exact linear relationship between them then we have perfect multicollinearity.

## What is the difference between Heteroscedasticity and Homoscedasticity?

The assumption of homoscedasticity (meaning “same variance”) is central to linear regression models. … Heteroscedasticity (the violation of homoscedasticity) is present when the size of the error term differs across values of an independent variable.

## Why Heteroscedasticity is a problem?

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.

## 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 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 test for Multicollinearity?

Multicollinearity can also be detected with the help of tolerance and its reciprocal, called variance inflation factor (VIF). If the value of tolerance is less than 0.2 or 0.1 and, simultaneously, the value of VIF 10 and above, then the multicollinearity is problematic.

## What does Homoscedasticity look like?

Homoscedasticity / Homogeneity of Variance/ Assumption of Equal Variance. Simply put, homoscedasticity means “having the same scatter.” For it to exist in a set of data, the points must be about the same distance from the line, as shown in the picture above.