A Fast Method for Implementing Hypothesis Tests with Multiple Sample Splits in Nonparametric Models of Production

Leopold Simar and Paul Wilson ()
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Paul Wilson: Clemson University

No 2024012, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: Kneip, Simar and Wilson (Journal of Business and Economic Statistics, 2016) and Daraio, Simar and Wilson (The Econometrics Journal, 2018) provide non-parametric tests of (i) convexity versus non-convexity of the production set, (ii) constant ver- sus non-constant returns-to-scale of the frontier, and (iii) separability versus non- separability of the frontier with respect to environmental variables. Among other uses, these tests are essential for deciding which non-parametric efficiency estimator should be used to estimate technical efficiency. Each test requires randomly splitting the sample. Although theory establishes that the tests are valid for any random split, results can vary with different splits. This paper provides a computationally efficient method to aggregate test outcomes across multiple sample-splits using ideas from the statistical literature on controlling false discovery rates in multiple testing situations. We provide tests using multiple sample-splits (to remove the ambiguity resulting from a single sample-split) and extensive Monte Carlo evidence on the size and power of our tests. The computational time required by the new tests is about 0.001 times the computational time required by the bootstrap method proposed by Simar and Wilson (Journal of Productivity Analysis, 2020).

Keywords: Hypothesis testing; inference; multiple splits; convexity; returns to scale; separability; DEA; FDH (search for similar items in EconPapers)
JEL-codes: C12 C44 C63 (search for similar items in EconPapers)
Pages: 78
Date: 2024-04-10
New Economics Papers: this item is included in nep-ecm and nep-eff
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