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Asymptotic properties of a spatial autoregressive stochastic frontier model

Fei Jin () and Lung-fei Lee ()
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Fei Jin: Fudan University, and Shanghai Institute of International Finance and Economics
Lung-fei Lee: Fudan University, and Shanghai Institute of International Finance and Economics

Journal of Spatial Econometrics, 2020, vol. 1, issue 1, 1-40

Abstract: Abstract This paper considers asymptotic properties of a spatial autoregressive stochastic frontier model. Relying on the asymptotic theory for nonlinear spatial NED processes, we prove the consistency and asymptotic distribution of the maximum likelihood estimator under regularity conditions. When inefficiency exists, all parameter estimators have the $$\sqrt{n}$$ n -rate of convergence and are asymptotically normal. However, when there is no inefficiency, only some parameter estimators have the $$\sqrt{n}$$ n -rate of convergence, and the rest have slower convergence rates. We also investigate a corrected two stage least squares estimator that is computationally simple, and derive the asymptotic distributions of the score and likelihood ratio test statistics that test for the existence of inefficiency.

Keywords: Stochastic frontier; Spatial autoregression; Maximum likelihood; Asymptotic property; Test (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 C51 R32 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s43071-020-00002-z

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