Instrumental Variable Quantile Estimation of Spatial Autoregressive Models

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 quantil...

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Main Authors: SU, Liangjun, YANG, Zhenlin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2011
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Online Access:https://ink.library.smu.edu.sg/soe_research/1074
https://ink.library.smu.edu.sg/context/soe_research/article/2073/viewcontent/Instrumental_Variable_Quantile_Estimation_2011_wp.pdf
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spelling sg-smu-ink.soe_research-20732019-04-27T05:39:13Z Instrumental Variable Quantile Estimation of Spatial Autoregressive Models SU, Liangjun YANG, Zhenlin 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. 2011-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1074 https://ink.library.smu.edu.sg/context/soe_research/article/2073/viewcontent/Instrumental_Variable_Quantile_Estimation_2011_wp.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Spatial Autoregressive Model IV Quantile Regression Instrumental Variable Spatial Dependence Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Spatial Autoregressive Model
IV Quantile Regression
Instrumental Variable
Spatial Dependence
Econometrics
spellingShingle Spatial Autoregressive Model
IV Quantile Regression
Instrumental Variable
Spatial Dependence
Econometrics
SU, Liangjun
YANG, Zhenlin
Instrumental Variable Quantile Estimation of Spatial Autoregressive Models
description 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.
format text
author SU, Liangjun
YANG, Zhenlin
author_facet SU, Liangjun
YANG, Zhenlin
author_sort SU, Liangjun
title Instrumental Variable Quantile Estimation of Spatial Autoregressive Models
title_short Instrumental Variable Quantile Estimation of Spatial Autoregressive Models
title_full Instrumental Variable Quantile Estimation of Spatial Autoregressive Models
title_fullStr Instrumental Variable Quantile Estimation of Spatial Autoregressive Models
title_full_unstemmed Instrumental Variable Quantile Estimation of Spatial Autoregressive Models
title_sort instrumental variable quantile estimation of spatial autoregressive models
publisher Institutional Knowledge at Singapore Management University
publishDate 2011
url https://ink.library.smu.edu.sg/soe_research/1074
https://ink.library.smu.edu.sg/context/soe_research/article/2073/viewcontent/Instrumental_Variable_Quantile_Estimation_2011_wp.pdf
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