Difference between revisions of "R reg diag"
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One of the Gauss-Markov assumption is that the variance of the regression error terms is constant. If they are not, then the OLS parameter estimators will not be efficient and one needs to use heteroskedasticity robust standard errors to obtain valid inference on regression coefficients (see [[R_robust_se]]). | One of the Gauss-Markov assumption is that the variance of the regression error terms is constant. If they are not, then the OLS parameter estimators will not be efficient and one needs to use heteroskedasticity robust standard errors to obtain valid inference on regression coefficients (see [[R_robust_se]]). | ||
| − | Tests for heteroskedasticity are usually based on an auxiliary regression of estimated squared regression residuals on a set of explanatory variables that are suspected to be related to the potentially changing error variance. We continue the example | + | Tests for heteroskedasticity are usually based on an auxiliary regression of estimated squared regression residuals on a set of explanatory variables that are suspected to be related to the potentially changing error variance. We continue the example we started in [[R_Regression#A First Example]] |
= Autocorrelation = | = Autocorrelation = | ||
Revision as of 08:08, 14 April 2015
When estimating regression models you will usually want to undertake some diagnostic testing. The functions we will use are all contained in the "AER" package (see the relevant CRAN webpage).
Heteroskedasticity
One of the Gauss-Markov assumption is that the variance of the regression error terms is constant. If they are not, then the OLS parameter estimators will not be efficient and one needs to use heteroskedasticity robust standard errors to obtain valid inference on regression coefficients (see R_robust_se).
Tests for heteroskedasticity are usually based on an auxiliary regression of estimated squared regression residuals on a set of explanatory variables that are suspected to be related to the potentially changing error variance. We continue the example we started in R_Regression#A First Example
