K
Kenneth D. West
Researcher at Princeton University
Publications - 282
Citations - 48922
Kenneth D. West is an academic researcher from Princeton University. The author has contributed to research in topics: Polariton & Monetary policy. The author has an hindex of 59, co-authored 245 publications receiving 44956 citations. Previous affiliations of Kenneth D. West include University of Pittsburgh & Alcatel-Lucent.
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A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
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A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
Posted Content
Automatic Lag Selection in Covariance Matrix Estimation
TL;DR: A nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix is proposed and proved to be asymptotically equivalent to one that is optimal under a mean squared error loss function.
Journal ArticleDOI
Automatic Lag Selection in Covariance Matrix Estimation
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this paper, a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix was proposed.
Journal ArticleDOI
Approximately Normal Tests for Equal Predictive Accuracy in Nested Models
TL;DR: In this paper, the mean squared prediction error (MSPE) from the parsimonious model is adjusted to account for the noise in the large model's model. But, the adjustment is based on the nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size.