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

Baojun Dou, Maria Lucia Parrella and Qiwei Yao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: α-mixing; dynamic panels; high dimensionality; least squares estimation; spatial autoregression; stationarity (search for similar items in EconPapers)
JEL-codes: C13 C23 C32 (search for similar items in EconPapers)
Date: 2016-10-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ure
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Citations: View citations in EconPapers (11)

Published in Journal of Econometrics, 1, October, 2016, 194(2), pp. 369-382. ISSN: 0304-4076

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:67151

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