A Newey–West estimator is used in statistics and econometrics to provide an estimate of the covariance matrix of the parameters of a regression-type model when this model is applied in situations where the standard assumptions of regression analysis do not apply. It was devised by Whitney K. Newey and Kenneth D. West in 1987, although there are a number of later variants. 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 problem in autocorrelation, often found in time series data, is that the error terms are correlated over time. This can be demonstrated in
Software implementations
In R, the sandwich
package includes a function for the Newey–West estimator.
In Stata, the command newey
procudes Newey–West standard errors for coefficients estimated by OLS regression.