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What is the purpose of HAC standard errors?
The estimator is used to try to overcome autocorrelation (also called serial correlation), and heteroskedasticity in the error terms in the models, often for regressions applied to time series data. The abbreviation “HAC,” sometimes used for the estimator, stands for “heteroskedasticity and autocorrelation consistent.”
What are heteroskedasticity robust standard errors?
Standard errors based on this procedure are called (heteroskedasticity) robust standard errors or White-Huber standard errors. Or it is also known as the sandwich estimator of variance (because of how the calculation formula looks like). Thus, the robust standard errors are appropriate even under homoskedasticity.
How to calculate HAC standard errors with R?
The package sandwich also contains the function NeweyWest (), an implementation of the HAC variance-covariance estimator proposed by Newey and West ( 1987) . Consider the distributed lag regression model with no lags and a single regressor Xt X t Y t =β0 +β1Xt +ut. Y t = β 0 + β 1 X t + u t. with autocorrelated errors.
What’s the idea behind HAC robust standard errors?
We got to appoint that HAC standard errors (also called HAC estimators) are derived from the work of Newey & West (1987) where the objective was to build a robust approach to handle the usual problems of time series associated with serial correlation and heteroskedasticity. What’s the idea behind these standard errors?
When to use HAC corrections in time series?
Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations.
How to calculate a HAC standard error in coeftest?
Of course, a variance-covariance matrix estimate as computed by NeweyWest () can be supplied as the argument vcov in coeftest () such that HAC t t -statistics and p p -values are provided by the latter. Newey, Whitney K., and Kenneth D. West. 1987.