Newey-west Standard Error Correction
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when this model is applied in situations where the standard assumptions of regression analysis do not apply.[1] It was devised by Whitney K. Newey and
Newey West Standard Errors Stata
Kenneth D. West in 1987, although there are a number of later newey west r variants.[2][3][4][5] The estimator is used to try to overcome autocorrelation (also called serial correlation), and heteroskedasticity in the
Newey West Matlab
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 newey west 1987 over time. This can be demonstrated in Q ∗ {\displaystyle Q*} , a matrix of sums of squares and cross products that involves σ ( i j ) {\displaystyle \sigma _{(ij)}} and the rows of X {\displaystyle X} . The least squares estimator b {\displaystyle b} is a consistent estimator of β {\displaystyle \beta } . This implies that newey west eviews the least squares residuals e i {\displaystyle e_{i}} are "point-wise" consistent estimators of their population counterparts E i {\displaystyle E_{i}} . The general approach, then, will be to use X {\displaystyle X} and e {\displaystyle e} to devise an estimator of Q ∗ {\displaystyle Q*} .[6] This means that as the time between error terms increases, the correlation between the error terms decreases. The estimator thus can be used to improve the ordinary least squares (OLS) regression when the residuals are heteroskedastic and/or autocorrelated. w ℓ = 1 − ℓ L + 1 {\displaystyle w_{\ell }=1-{\frac {\ell }{L+1}}} See also[edit] Heteroscedasticity-consistent standard errors References[edit] ^ "Newey West estimator – Quantitative Finance Collector". ^ Newey, Whitney K; West, Kenneth D (1987). "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix". Econometrica. 55 (3): 703–708. doi:10.2307/1913610. JSTOR1913610. ^ Andrews, Donald W. K. (1991). "Heteroskedasticity and autocorrelation consistent covariance matrix estimation". Econometrica. 59 (3): 817–858. doi:10.2307/2938229. JSTOR2938229. ^ Newey, Whitney K.; West, Kenneth D. (1994). "Automatic lag selection in covariance matrix estimation". Review of Economic Studies. 61 (4): 63
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