Lag order selection for an optimal autoregressive covariance matrix estimator

A good parametric spectral estimator requires an accurate estimate of the sum of AR coefficients, however a criterion which minimizes the innovation variance not necessarily yields the best spectral estimate. This paper develops an alternative information criterion considering the bias in the sum of the parameters for the autoregressive estimator of the spectral density at frequency zero.

MoreLess

Year of publication:	2010
Authors:	Morales, Marco
Published in:	Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 37.2010, 5, p. 739-748
Publisher:	Taylor & Francis Journals
Subject:	Spectral density \| covariance matrix \| autoregressive \| lag-order selection \| statistical inference

More details

Extent:	text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10008674913