# What is the WHITE's Trend Test?

White's Test for Heteroskedasticity, also known as White's General Test or White's Trend Test, is a statistical test used to detect heteroscedasticity in a regression analysis. Heteroscedasticity refers to the situation where the variance of the errors (residuals) in a regression model is not constant across all levels of the independent variables.

White's Test is a general test for heteroscedasticity, meaning it can detect a wide range of patterns in the variance of the errors, including non-linear relationships between the predictors and the variance of the residuals. It is a popular test because it is robust and flexible.

Here are the main steps involved in White's Test:

1. Run a Regression: First, you need to estimate your regression model using ordinary least squares (OLS) regression. Obtain the residuals from the regression model.

2. Square the Residuals: Calculate the squared residuals from the regression model. These squared residuals represent the squared deviations of the observed values from the predicted values.

3. Run an Auxiliary Regression: Regress the squared residuals on the independent variables and any other relevant terms that might affect the variance of the errors. This auxiliary regression helps to capture patterns in the variance of the residuals that are not explained by the original regression model.

4. Test for Significance: Conduct a test of the joint significance of the independent variables in the auxiliary regression. The test statistic follows a chi-squared distribution under the null hypothesis of homoscedasticity (constant variance of the errors). If the test statistic is statistically significant, it suggests the presence of heteroscedasticity.

The advantage of White's Test is that it does not impose specific functional forms on the relationship between the independent variables and the variance of the errors. Instead, it allows for more flexibility in detecting heteroscedasticity.

White's Test is widely used in econometrics and other fields where regression analysis is common. It helps researchers identify violations of the homoscedasticity assumption, which is crucial for ensuring the validity of regression results.

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