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Newey–West estimator

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 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, 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." There are a number of HAC estimators described in, and HAC estimator does not refer uniquely to Newey–West. One version of Newey–West Bartlett requires the user to specify the bandwidth and usage of the Bartlett kernel from Kernel density estimation

Software implementations
In Julia, the CovarianceMatrices.jl package supports several types of heteroskedasticity and autocorrelation consistent covariance matrix estimation including Newey–West, White, and Arellano. In R, the packages sandwich and plm include a function for the Newey–West estimator. In Stata, the command newey produces Newey–West standard errors for coefficients estimated by OLS regression. In MATLAB, the command hac in the Econometrics toolbox produces the Newey–West estimator (among others). In Python, the statsmodels module includes functions for the covariance matrix using Newey–West. In Gretl, the option --robust to several estimation commands (such as ols) in the context of a time-series dataset produces Newey–West standard errors. In SAS, the Newey–West corrected standard errors can be obtained in PROC AUTOREG and PROC MODEL == See also ==
Source: Wikipedia ↗
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