Generalized Yule-Walker Estimation for Spatio-Temporal Models with Unknown Diagonal Coefficients

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

26 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)369-382
Journal / PublicationJournal of Econometrics
Volume194
Issue number2
Online published30 May 2016
Publication statusPublished - Oct 2016
Externally publishedYes

Link(s)

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.

Research Area(s)

  • α-mixing, Dynamic panels, High dimensionality, Least squares estimation, Spatial autoregression, Stationarity

Citation Format(s)

Generalized Yule-Walker Estimation for Spatio-Temporal Models with Unknown Diagonal Coefficients. / Dou, Baojun; Parrella, Maria Lucia; Yao, Qiwei .
In: Journal of Econometrics, Vol. 194, No. 2, 10.2016, p. 369-382.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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