Publication Type

Journal Article

Version

Preprint

Publication Date

7-2010

Abstract

We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are partially linear. The rate of convergence of the spatial parameter estimator depends on some general features of the spatial weight matrix of the model. The estimators of other finite-dimensional parameters in the model have the regular √n-rate of convergence and the estimator of the nonparametric component is consistent but with different restrictions on the choice of bandwidth parameter associated with different natures of the spatial weights. Monte Carlo simulations verify our theory and indicate that our estimators perform reasonably well in finite samples.

Keywords

Profile likelihood, Partially linear models, Quasi-maximum likelihood estimation, Spatial autoregression, Spatial dependence

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

157

Issue

1

First Page

18

Last Page

33

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2009.10.033

Publisher

Elsevier

Copyright Owner and License

Authors

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

https://doi.org/10.1016/j.jeconom.2009.10.033

Included in

Econometrics Commons

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