Nonparametric Statistical Analysis of Production
(2020) The Palgrave Handbook of Economic Performance Analysis — ISBN: [978-3-030-23726-4], p. 301-381, published
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- Abstract
- A rich literature on the analysis of efficiency in production has developed since pi- oneering work of Tjalling Koopmans and George Debreu in the 1950s. This literature includes work by researchers in economics, econometrics, management science, opera- tions research, mathematical statistics and other fields. The focus in this survey is on nonparametric approaches for estimation and inference, with particular emphasis on in- ference since nothing can be learned from estimation without inference. The statistical problem amounts to estimating the support of a multivariate random variable, subject to some shape constraints, in multiple dimensions. New results that enable inference about mean efficiency as well as tests of convexity, returns to scale, differences in mean efficiency and the separability condition described by Simar and Wilson (2007) are discussed. The well-known curse of dimensionality presents additional challenges, but recent work indicates that reducing dimensionality using eigensystem techniques may improve estimation accuracy. Remaining challenges and open issues are also discussed.
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Mastromarco, C., Simar, L., & Wilson, P. (2020). Nonparametric Statistical Analysis of Production. In Thijs ten Raa, William H. Greene (ed.), The Palgrave Handbook of Economic Performance Analysis (p. p. 301-381). Springer International Publishing. https://doi.org/10.1007/978-3-030-23727-1