Smith, Richard J. (2005) Automatic positive semidefinate HAC covariance matrix and GMM estimation. Econometric Theory, Vol.21 (No.1). pp. 158-170. doi:10.1017/S0266466605050103

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Abstract

This paper proposes a new class of heteroskedastic and autocorrelation consistent (HAC) covariance matrix estimators. The standard HAC estimation method reweights estimators of the autocovariances. Here we initially smooth the data observations themselves using kernel function–based weights. The resultant HAC covariance matrix estimator is the normalized outer product of the smoothed random vectors and is therefore automatically positive semidefinite. A corresponding efficient GMM criterion may also be defined as a quadratic form in the smoothed moment indicators whose normalized minimand provides a test statistic for the overidentifying moment conditions.

Item Type:	Journal Article
Subjects:	H Social Sciences > HB Economic Theory Q Science > QA Mathematics
Divisions:	Faculty of Social Sciences > Economics
Library of Congress Subject Headings (LCSH):	Analysis of covariance, Estimation theory, Econometrics, Heteroscedasticity, Equivalence relations (Set theory)
Journal or Publication Title:	Econometric Theory
Publisher:	Cambridge University Press
ISSN:	0266-4666
Official Date:	February 2005
Dates:	Date Event February 2005 Published
Volume:	Vol.21
Number:	No.1
Page Range:	pp. 158-170
DOI:	10.1017/S0266466605050103
Status:	Peer Reviewed
Access rights to Published version:	Open Access

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