Semiparametric estimation of a spatial autoregressive nonparametric stochastic frontier model

Kien Tran and Mike G. Tsionas ()
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Mike G. Tsionas: Lancaster University Management School

Journal of Spatial Econometrics, 2023, vol. 4, issue 1, 1-28

Abstract: Abstract This paper proposes a semiparametric spatial autoregressive stochastic frontier model where the spatial lag on the dependent variable enters linearly whilst the functional form of the frontier is modeled nonparametrically. A three-step estimation procedure is considered where in the first two steps, a constrained (i.e., shape restrictions) semiparametric profile generalized method of moments that is based on the localized instruments of exogenous variables in the model and their spatial weighted version is used to obtain the estimates of the spatial parameter and the unknown smooth function of the frontier; whilst in the final step, the remaining parameters of the model can be estimated using maximum likelihood procedure. We derive the limiting distributions of the proposed estimators for both parametric and nonparametric components in the model. Monte Carlo simulations reveal that our proposed estimators perform well in finite samples. An empirical application of the Chinese Chemical firms is presented to illustrate the usefulness of the proposed approach in practice.

Keywords: Inefficiency spillovers; Maximum likelihood; Nonparametric frontier; Profile GMM; Shape restrictions; Spatial autoregressive (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 C21 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43071-023-00036-z

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