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Allowing a general spatial linear process form for the disturbances that permits many common types of error specifications as well as potential \u2018long memory\u2019, 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.<\/jats:p>","DOI":"10.1017\/s0266466618000142","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T06:28:52Z","timestamp":1525328932000},"page":"417-463","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":12,"title":["ESTIMATION OF SPATIAL AUTOREGRESSIONS WITH STOCHASTIC WEIGHT MATRICES"],"prefix":"10.1017","volume":"35","author":[{"given":"Abhimanyu","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2018,5,3]]},"reference":[{"key":"S0266466618000142_ref39","unstructured":"Yuzefovich Y.A . (2003) Two Essays on Spatial Econometrics. 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