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Estimation of Spatial Autoregressions with Stochastic Weight Matrices

Gupta, A (2015) Estimation of Spatial Autoregressions with Stochastic Weight Matrices. Working Paper. University of Essex, Department of Economics, Economics Discussion Papers. (Unpublished)


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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 and ordinary least squares 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 and suggests, like the theory, that spatial weights generated from distributions with ?smaller? moments yield better estimates. Our results are especially pertinent in situations where spatial weights are functions of stochastic economic variables.

Item Type: Monograph (Working Paper)
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Economics, Department of
Depositing User: Jim Jamieson
Date Deposited: 11 Dec 2015 14:45
Last Modified: 20 Mar 2018 14:03

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