Research Repository

Estimation of spatial autoregressions with stochastic weight matrices

Gupta, Abhimanyu (2019) 'Estimation of spatial autoregressions with stochastic weight matrices.' Econometric Theory, 35 (2). 417 - 463. ISSN 0266-4666

merged_accepted.pdf - Accepted Version

Download (384kB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: Spatial autoregression, stochastic spatial weights, spatial linear process, instrumental variables, ordinary least squares, Gaussian pseudo maximum likelihood
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Economics, Department of
Depositing User: Elements
Date Deposited: 20 Mar 2018 14:07
Last Modified: 19 Jun 2020 14:15

Actions (login required)

View Item View Item