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What does robust standard error tell us?
“Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. “Robust” standard errors have many labels that essentially refer all the same thing. Namely, standard errors that are computed with the sandwich estimator of variance.
How does autocorrelation affect standard errors?
From the Wikipedia article on autocorrelation: While it does not bias the OLS coefficient estimates, the standard errors tend to be underestimated (and the t-scores overestimated) when the autocorrelations of the errors at low lags are positive.
What do Newey West standard errors do?
The Newey-West method handles autocorrelation with lags up to h, and so it is assumed that lags larger than h can be ignored. Note too that Newey-West not only corrects for autocorrelation it also corrects for heteroscedasticity (heterogeneity of variances).
When to use robust standard errors in statistics?
Caution: When robust standard errors are used, the F-statistic (cell K12 in Figure 2) is not accurate and so it and the corresponding p-value should not be relied on. Real Statistics Function: The following array function computes the coefficients and their standard errors for weighted linear regression.
What does autocorrelation mean for a model error?
Autocorrelated errors signal model misspecification. Ideally, model errors should be i. i. d. and thus should have no patterns in them. If they do, there is some information left unextracted; some more modelling can be done to extract the pattern.
How to correct for autocorrelation in simple linear regression?
I have found autocorrelation occuring in some of my linear regression models, but I haven’t got such a simple approach to correcting for the autocorrelation. Most answers go into complex equations (with packages like “spdep”, “sphet”, “gmm”, etc.) rather than straight forward R commands to run corrective models.
What are the pros and cons of autocorrelation?
Cons: wider confidence intervals → lower precision, less power (harder to reject null hypotheses); model is misspecified; less accurate forecasting (due to neglecting the autocorrelation in model errors). Change the model specification to obtain non-autocorrelated errors.