Estimation and inference in heterogeneous spatial panels with a multifactor error structure
Estimation and inference in heterogeneous spatial panels with a multifactor error structure
We develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and common factors. To tackle the challenging issues of endogeneity due to the spatial lagged term and the correlation between the regressors and factors, we propose the CCEX-IV estimation procedure that approximates factors by the cross-section averages of regressors and deals with the spatial endogeneity using the internal instrumental variables. We develop the individual and Mean Group estimators, and establish their consistency and asymptotic normality. By contrast, the Pooled estimator is shown to be inconsistent in the presence of parameter heterogeneity. Monte Carlo simulations confirm that the finite sample performance of the proposed estimators is quite satisfactory. We demonstrate the usefulness of our approach with an application to the house price growth for Local Authority Districts in the UK over 1997Q1–2016Q4.
55-79
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Shin, Yongcheol
ef5f55b9-256b-411a-986e-e4b4ee07716d
Zheng, Chaowen
4ba693c1-6dd0-45b1-acf1-45bfb393f3fc
28 April 2022
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Shin, Yongcheol
ef5f55b9-256b-411a-986e-e4b4ee07716d
Zheng, Chaowen
4ba693c1-6dd0-45b1-acf1-45bfb393f3fc
Chen, Jia, Shin, Yongcheol and Zheng, Chaowen
(2022)
Estimation and inference in heterogeneous spatial panels with a multifactor error structure.
Journal of Econometrics, 229 (1), .
(doi:10.1016/j.jeconom.2021.05.003).
Abstract
We develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and common factors. To tackle the challenging issues of endogeneity due to the spatial lagged term and the correlation between the regressors and factors, we propose the CCEX-IV estimation procedure that approximates factors by the cross-section averages of regressors and deals with the spatial endogeneity using the internal instrumental variables. We develop the individual and Mean Group estimators, and establish their consistency and asymptotic normality. By contrast, the Pooled estimator is shown to be inconsistent in the presence of parameter heterogeneity. Monte Carlo simulations confirm that the finite sample performance of the proposed estimators is quite satisfactory. We demonstrate the usefulness of our approach with an application to the house price growth for Local Authority Districts in the UK over 1997Q1–2016Q4.
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Accepted/In Press date: 11 May 2021
e-pub ahead of print date: 3 June 2021
Published date: 28 April 2022
Identifiers
Local EPrints ID: 484866
URI: http://eprints.soton.ac.uk/id/eprint/484866
ISSN: 0304-4076
PURE UUID: e409405d-eac3-4115-8f7f-f9beae43ef60
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Date deposited: 23 Nov 2023 17:56
Last modified: 18 Mar 2024 04:15
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Author:
Jia Chen
Author:
Yongcheol Shin
Author:
Chaowen Zheng
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