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Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients

Dou, Baojun, Parrella, Maria Lucia and Yao, Qiwei (2016) Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients. Journal of Econometrics, 194 (2). pp. 369-382. ISSN 0304-4076

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Identification Number: 10.1016/j.jeconom.2016.05.014

Abstract

We consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. To overcome the innate endogeneity, we propose a generalized Yule–Walker estimation method which applies the least squares estimation to a Yule–Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations (or panels) tend to infinity under a general setting for stationary and α-mixing processes, which includes spatial autoregressive panel data models driven by i.i.d. innovations as special cases. The proposed methods are illustrated using both simulated and real data.

Item Type: Article
Official URL: http://www.elsevier.com/locate/issn/03044076
Additional Information: © 2016 The Authors © CC BY 4.0
Divisions: Statistics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation
C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C23 - Models with Panel Data
C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models
Sets: Departments > Statistics
Collections > Economists Online
Date Deposited: 18 Jul 2016 09:53
Last Modified: 20 Jun 2019 02:20
Projects: EP/L01226X/1, 2010J3LZEN
Funders: Engineering and Physical Sciences Research Council, Italian Ministry of Education, University and Research, PRIN Research Project
URI: http://eprints.lse.ac.uk/id/eprint/67151

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