F test in stata how to#
Check out this tutorial to learn about how to use robust standard errors in regression in Stata.I am trying to export the F-statistic and Prob > F for 2 tests of coefficients (for many regressions each). Use robust standard errors. Robust standard errors are more “robust” to the problem of heteroscedasticity and tend to provide a more accurate measure of the true standard error of a regression coefficient. When the proper weights are used, this can eliminate the problem of heteroscedasticity.ģ. Essentially, this gives small weights to data points that have higher variances, which shrinks their squared residuals. Use weighted regression. This type of regression assigns a weight to each data point based on the variance of its fitted value. Another common transformation is to use the square root of the response variable.Ģ. Typically taking the log of the response variable is an effective way of making heteroscedasticity go away. For example, you could use log(price) instead of price as the response variable. Transform the response variable. You can try performing a transformation on the response variable. There are several ways that you can fix this issue, including:ġ. In this case, the standard errors that are shown in the output table of the regression are unreliable. However, if you reject the null hypothesis of the Breusch-Pagan test, this means heteroscedasticity is present in the data. If you fail to reject the null hypothesis of the Breusch-Pagan test, then heteroscedasticity is not present and you can proceed to interpret the output of the original regression. Since this value is less than 0.05, we can reject the null hypothesis and conclude that heteroscedasticity is present in the data. Prob > chi2: This is the p-value that corresponds to the Chi-Square test statistic. In this case, it was the variable price.Ĭhi2(1): This is the Chi-Square test statistic of the test. Variables: This tells us the response variable that was used in the regression model. Ho: This is the null hypothesis of the test, which states that there is constant variance among the residuals.
Once we fit the regression model, then we can perform the Breusch-Pagan Test using the hettest command, which is short for “heteroscedasticity test”: Next, we will type in the following command to perform a multiple linear regression using price as the response variable and mpg and weight as the explanatory variables: Step 2: Perform multiple linear regression. Then, view the raw data by using the following command: We will use the built-in Stata dataset auto to illustrate how to perform the Breusch-Pagan Test.įirst, use the following command to load the data: This tutorial explains how to perform a Breusch-Pagan Test in Stata. If the p-value is below a certain threshold (common choices are 0.01, 0.05, and 0.10) then there is sufficient evidence to say that heteroscedasticity is present. This test produces a Chi-Square test statistic and a corresponding p-value. One test that we can use to determine if heteroscedasticity is present is the Breusch-Pagan Test. Unfortunately, one problem that often occurs in regression is known as heteroscedasticity, in which there is a systematic change in the variance of residuals over a range of measured values. Multiple linear regression is a method we can use to understand the relationship between several explanatory variables and a response variable.
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