Publication Type
Journal Article
Version
submittedVersion
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
Citation
SU, Liangjun and JIN, Sainan.
Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models. (2010). Journal of Econometrics. 157, (1), 18-33.
Available at: https://ink.library.smu.edu.sg/soe_research/1278
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2009.10.033