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Persistent and transient inefficiency in a spatial autoregressive panel stochastic frontier model

Hung-pin Lai () and Kien Tran ()

Journal of Productivity Analysis, 2022, vol. 58, issue 1, No 1, 13 pages

Abstract: Abstract In this paper, we extend the four-component stochastic frontier model to allow for global spatial dependence via the endogenous spatial autoregressive variable. Our proposed model is more general than the model considered by (Glass et al., 2016) in the sense that we include a random effect as well as a permanent efficiency component. With the spatial autoregressive specification, our model is able to capture the asymmetric efficiency spillovers and also decompose the persistent/transient inefficiencies into direct and indirect efficiencies. Moreover, we also investigate the marginal effects of the exogenous variables on the persistent/transient efficiency. We suggest a maximum simulated likelihood method to estimate the frontier parameters of the model, and we predict the efficiencies using the simulated estimator. Monte Carlo simulations reveal that the suggested estimator performs well in finite samples. An empirical application is considered to illustrate the usefulness of our proposed model and method.

Keywords: Maximum simulated likelihood; stochastic frontier; spatial autoregressive; persistent inefficiency; transient inefficiency (search for similar items in EconPapers)
JEL-codes: C23 C51 D24 E23 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11123-022-00638-z

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