## How do you test for heteroskedasticity in Stata?

Figure 5: Testing for Heteroscedasticity Using the Postestimation Selector Dialog Box in Stata. 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.

**How do you test for heteroskedasticity white?**

Follow these five steps to perform a White test:

- Estimate your model using OLS:
- Obtain the predicted Y values after estimating your model.
- Estimate the model using OLS:
- Retain the R-squared value from this regression:
- Calculate the F-statistic or the chi-squared statistic:

### What statistical test do you use for 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?**

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 read heteroskedasticity test?

One of the most common ways of checking for heteroskedasticity is by plotting a graph of the residuals. Visually, if there appears to be a fan or cone shape in the residual plot, it indicates the presence of heteroskedasticity.

**What does the White test test for?**

In statistics, the White test is a statistical test that establishes whether the variance of the errors in a regression model is constant: that is for homoskedasticity. This test, and an estimator for heteroscedasticity-consistent standard errors, were proposed by Halbert White in 1980.

## What are the advantages of the White test for heteroskedasticity?

White’s test for Heteroskedasticity White test for Heteroskedasticity is general because it does not rely on the normality assumptions and it is also easy to implement. Because of the generality of White’s test, it may identify the specification bias too.

**What is an auxiliary regression?**

Auxiliary Regression: A regression used to compute a test statistic-such as the test statistics for heteroskedasticity and serial correlation or any other regression that does not estimate the model of primary interest.

### Does heteroskedasticity cause inconsistency?

plays no role in showing whether OLS was unbiased or consistent. If heteroskedasticity does not cause bias or inconsistency in the OLS estimators, why did we introduce it as one of the Gauss-Markov assumptions? The estimators of the variances, V (ˆβj), are biased without the homoskedasticity assumption.

**What is the problem with heteroskedasticity?**

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. In addition, the OLS estimator is no longer BLUE.