Publication Type
Working Paper
Version
publishedVersion
Publication Date
5-2011
Abstract
We propose a spatial quantile autoregression (SQAR) model, which allows cross-sectional dependence among the responses, unknown heteroscedasticity in the disturbances, and heterogeneous impacts of covariates on different points (quantiles) of a response distribution. The instrumental variable quantile regression (IVQR) method of Chernozhukov and Hansen (2006) is generalized to allow the data to be non-identically distributed and dependent, an IVQR estimator for the SQAR model is then defined, and its asymptotic properties are derived. Simulation results show that this estimator performs well in finite samples at various quantile points. In the special case of spatial median regression, it outperforms the conventional QML estimator without taking into account of heteroscedasticity in the errors; it also outperforms the GMM estimators with or without heteroscedasticity. An empirical illustration is provided.
Keywords
Spatial Autoregressive Model, IV Quantile Regression, Instrumental Variable, Spatial Dependence
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
35
Citation
SU, Liangjun and YANG, Zhenlin.
Instrumental Variable Quantile Estimation of Spatial Autoregressive Models. (2011). 1-35.
Available at: https://ink.library.smu.edu.sg/soe_research/1074
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.