Contents
What is Park test for heteroskedasticity?
The Park Test is a test for heteroscedasticity. Heteroscedasticity means that the variances of the errors are not the same across a set of independent (predictor) variables. Use the Park test for heteroscedasticity if you have some variable Z that you think might explain the different variances of the residuals.
Who created the park test?
Rolla Edward Park
In econometrics, the Park test is a test for heteroscedasticity. The test is based on the method proposed by Rolla Edward Park for estimating linear regression parameters in the presence of heteroscedastic error terms.
How do you do the Goldfeld Quandt test?
Steps for Running the Test Usually, ordering by magnitude of x (one independent variable) works. Divide your data into three parts*. Drop the observations in the middle part. Run separate regression analysis on the top and bottom parts (in other words, the groups with high values of x and low values of x).
When to use the park test for heteroscedasticity?
Use the Park test for heteroscedasticity if you have some variable Z that you think might explain the different variances of the residuals. There are different forms of this test: the log form is the most common and is the one outlined here, where: LN (Residual 2) = b (intercept) + slope (m) (LN (X)).
How to run a park test on data?
Step 1 : Run ordinary least squares on your data. Make sure the regression produces a table of residuals. Step 2 : Square the residuals from Step 1. Step 3 : Take the natural log of the squared residuals from Step 2. Step 4 : Take the natural log of Z, the variable which you suspect is causing the heteroscedastic behavior.
Which is the best form of the park test?
There are different forms of this test: the log form is the most common and is the one outlined here, where: LN (Residual 2) = b (intercept) + slope (m) (LN (X)). However, you can theoretically use another form, like the linear form Residual 2 = b + m (X).
How to check for heteroskedasticity in multivariate OLS?
One way to visually check for heteroskedasticity is to plot predicted values against residuals This works for either bivariate or multivariate OLS. If heteroskedasticity is suspected to derive from a single variable, plot it against the residuals