Erratum to Finite-sample bias of the QMLE in spatial autoregressive models created by Yong Bao

By:

Bao, Yong [author]

Material type: Text

TextSeries: Econometric theory ; Volume 29, number 1Cambridge: Cambridge University Press, 2013Content type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

Spatial autoregressive model

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Abstract: We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly applicable. In some special cases, the analytical bias result can be significantly simplified. Our Monte Carlo results demonstrate that the feasible bias-correction procedure works remarkably well.

Reviews from LibraryThing.com:

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library - Special Collections	HB139.T52 ECO (Browse shelf(Opens below))	vol. 29, no. 1 (pages 89-90)	SP17546	Not for loan	For in house use

Browsing Main Library shelves, Shelving location: - Special Collections Close shelf browser (Hides shelf browser)

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	HB139.T52 ECO First-order asymptotic theory for parametric misspecification tests of garch models	HB139.T52 ECO Oracle efficient variable selection in random and fixed effects panel data models	HB139.T52 ECO Nonparametric tests of moment condition stability	HB139.T52 ECO Erratum to Finite-sample bias of the QMLE in spatial autoregressive models	HB139.T52 ECO Time series regression on integrated continuous-time processes with heavy and light tails	HB139.T52 ECO Nonparametric cointegrating regression with NNH errors	HB139.T52 ECO The ET interview: professor George Judge	Next

We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly applicable. In some special cases, the analytical bias result can be significantly simplified. Our Monte Carlo results demonstrate that the feasible bias-correction procedure works remarkably well.

There are no comments on this title.

to post a comment.

here