Difference between revisions of "R reg diag"
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| − | When estimating regression models you will usually want to undertake some diagnostic testing | + | 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 |
= Heteroskedasticity = | = 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]]). | 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]]). | ||
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= Autocorrelation = | = Autocorrelation = | ||
Revision as of 15:39, 13 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
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).
