Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives

By Léopold Simar, Paul Wilson. 2013

323 citations recorded by the source corpus.

Nonparametric estimators are widely used to estimate the productive efficiency of firms and other organizations, but often without any attempt to make statistical inference. Recent work has provided statistical properties of these estimators as well as methods for making statistical inference, and a link between frontier estimation and extreme value theory has been established. New estimators that avoid many of the problems inherent with traditional efficiency estimators have also been developed; these new estimators are robust with respect to outliers and avoid the well-known curse of dimensionality. Statistical properties, including asymptotic distributions, of the new estimators have been uncovered. Finally, several approaches exist for introducing environmental variables into production models; both two-stage approaches, in which estimated efficiencies are regressed on environmental variables, and conditional efficiency measures, as well as the underlying assumptions required for either approach, are examined.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/88b40f7397b8057fbc869bd714e5042be9f5cbee

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Citations in the corpus

WHEN BIAS KILLS THE VARIANCE: CENTRAL LIMIT THEOREMS FOR DEA AND FDH EFFICIENCY SCORES (2014, methodology): “Simar and Wilson (2007, 2011b) observed that hundreds of papers have used this approach for explaining inefficiency in terms of environmental variables, and propose a well-defined, coherent statistical model in which the second stage regression is meaningful.”
Unobserved heterogeneity and endogeneity in nonparametric frontier estimation (2016, methodology): “Today, these nonparametric estimators have been analyzed from a statistical point of view and inference on efficiency estimates is available, mainly by using bootstrap techniques (see the recent survey Simar and Wilson, 2013 for details, and the references therein).”
Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non‐parametric, two‐stage models of production (2018, background): “It is well known that the distribution of US bank sizes is heavily skewed to the right; in fact, the distribution of total assets of US banks is roughly log–log-normal; see, e.g., Wheelock and Wilson (2001) for a discussion.”
Testing Hypotheses in Nonparametric Models of Production (2016, background): “Various assumptions on the production set can be made, but typical assumptions (e.g., Shephard 1970; Färe 1988; Simar and Wilson 2000) include the following.”
Effect of FDI and Time on Catching Up: New Insights from a Conditional Nonparametric Frontier Analysis (2015, unknown): “The rates of convergence are corrupted by the number of inputs and outputs, illustrating the curse of dimensionally of most of nonparametric methods (see Simar and Wilson 2013, for a recent survey).”
A Comparison of Performance of Islamic and Conventional Banks 2004 to 2009 (2014, methodology): “11 Bias corrected efficiencies are calculated using the homogeneous bootstrapping algorithm of (Simar and Wilson 2008).”