Publication Type

Journal Article

Publication Date

5-2015

Abstract

In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as it leads potentially to much improved inferences for the regression coefficients. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe, and the proposed bias correction procedure is very effective.

Keywords

asymptotics, bias correction, bootstrap, concentrated estimating equation, Monte Carlo, spatial layout, stochastic expansion

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometrics

Volume

3

Issue

2

First Page

376

Last Page

411

ISSN

2225-1146

Identifier

10.3390/econometrics3020376

Publisher

MDPI

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.3390/econometrics3020376

Included in

Econometrics Commons

Share

COinS