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These moments generate an estimator that is asymptotically more efficient than the quasi\u2010maximum likelihood estimator when the disturbances follow a non\u2010normal and unknown distribution. We apply this procedure to a high\u2010order spatial autoregressive model with spatial errors, where the disturbances are heteroskedastic. Two normality tests of disturbances are developed. We apply the model to employment data in US counties, which demonstrates spatial interdependence patterns of regional employment growth.<\/jats:p>","DOI":"10.1002\/jae.3046","type":"journal-article","created":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T13:05:15Z","timestamp":1710594315000},"page":"640-658","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Best linear and quadratic moments for spatial econometric models with an application to spatial interdependence patterns of employment growth in US counties"],"prefix":"10.1002","volume":"39","author":[{"given":"Fei","family":"Jin","sequence":"first","affiliation":[{"name":"School of Economics Fudan University, and Shanghai Institute of International Finance and Economics  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lung\u2010fei","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Economics Shanghai 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