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ESTIMATION OF SPATIAL AUTOREGRESSIONS WITH STOCHASTIC WEIGHT MATRICES

Published online by Cambridge University Press:  03 May 2018

Abhimanyu Gupta
Abhimanyu Gupta*
Affiliation:
University of Essex
*
*Address correspondence to Abhimanyu Gupta, Department of Economics, University of Essex; e-mail: a.gupta@essex.ac.uk.
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Abstract

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We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weight matrices. Allowing a general spatial linear process form for the disturbances that permits many common types of error specifications as well as potential ‘long memory’, we provide sufficient conditions for consistency and asymptotic normality of instrumental variables, ordinary least squares, and pseudo maximum likelihood estimates. The implications of popular weight matrix normalizations and structures for our theoretical conditions are discussed. A set of Monte Carlo simulations examines the behaviour of the estimates in a variety of situations. Our results are especially pertinent in situations where spatial weights are functions of stochastic economic variables, and this type of setting is also studied in our simulations.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 35 , Issue 2 , April 2019, pp. 417 - 463
Copyright
Copyright © Cambridge University Press 2018 

Footnotes

I am grateful to co-editor Guido Kuersteiner and three anonymous referees for excellent comments that improved the article substantially. I thank Peter Robinson for several comments and am also grateful to Nicolas Debarsy, Javier Hidalgo, Ingmar Prucha, and Renata Rabovic for useful suggestions and discussions.

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