HAC Estimation by Automated Regression

Peter Phillips

No 1470, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: A simple regression approach to HAC and LRV estimation is suggested. The method exploits the fact that the quantities of interest relate to only one point of the spectrum (the origin). The new estimator is simply the explained sum of squares in a linear regression whose regressors are a set of trend basis functions. Positive definiteness in the estimate is therefore automatically enforced and the technique can be implemented with standard regression packages. No kernel choice is needed in practical implementation but basis functions need to be chosen and a smoothing parameter corresponding to the number of basis functions needs to be selected. An automated approach to making this selection based on optimizing the asymptotic mean squared error is derived. The limit theory of the new estimator shows that its properties, including the convergence rate, are comparable to those of conventional HAC estimates constructed from quadratic kernels.

Keywords: Asymptotic mean squared error; automation; bias; HAC estimation; long run variance; trend regression; trigonometric polynomial (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2004-07
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: CFP 1158
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Published in Econometric Theory (2005), 21(1): 116-147

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