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Always Report Robust (White) Standard Errors? - Cross Validated Using robust standard errors has become common practice in economics Robust standard errors are typically larger than non-robust (standard?) standard errors, so the practice can be viewed as an effort to be conservative In large samples (e g , if you are working with Census data with millions of observations or data sets with "just" thousands of observations), heteroskedasticity tests will
Can robust standard errors be less than those from normal OLS? The above stream of robust statistcs generally recommends let observations get donweighted by the procedure (no outlier removal) There are other streams of robust statistcs that work on methods for detecting outliers But detecting outliers is subject to uncertainty (just like hypothesis testing) If you remove outliers, inference based on the remaining observation may be biased and has to be
Why Do Residuals Need To Be Homoscedastic (Equal Variance)? Lack of hetroscedastic can also indicate a poorly chosen model or a model missing key parameter (s) The hetroscedastic assumption is to ensure your prediction is equally accurate across the range of the model If the variance changes based on the value of the independent variable then the prediction will go from good to bad without any warning to the user
Robust regression inference and Sandwich estimators Can you give me an example of the use of sandwich estimators in order to perform robust regression inference? I can see the example in ?sandwich, but I don't quite understand how we can go from lm
Why does heteroskedasticity not affect $R^2$ and why does it make . . . As to 4, that is indeed, while empirically often the case, not necessarily true Consider as a tractable example the expressions for the standard and robust variance estimator (the correct one under heteroskedasticity) for a regression on a constant and a dummy investigated in these answers: Eicker-Huber-White Robust Variance Estimator and How to prove equality of standard errors for two
Interpreting Rs ur. df (Dickey-Fuller unit root test) results These are formulas for Dickey-Fuller test ur df performs ADF (Augmented Dickey-Fuller), which means you have to add also a $\Delta y_ {t-1}$ term to the regression It is also reflected by the output of ur df (summary () called on it): you can see that there is an additional regressor z diff lag