Showing papers by "Paul W. Wilson published in 2015"

Statistical Approaches for Nonparametric Frontier Models: A Guided Tour

Abstract: A rich theory of production and analysis of productive eciency has developed since pioneering work by Koopmans (1951) and Debreu (1951). Farrell (1957) is the earliest published empirical study, and appeared in a statistical journal (JRSS), even though Farrell provided no statistical theory. The literature in econometrics, management sciences, operations research and mathematical statistics has since been enriched by hundreds of papers trying to develop or implement new tools for analyzing productivity and efficiency of firms. Both parametric and nonparametric approaches have been proposed. The mathematical challenge is to derive estimators of production, cost, revenue,or profit frontiers which represent, in the case of production frontiers, the optimal loci of combinations of inputs (like labor, energy, capital, etc.) and outputs (the products or services produced by the firms). Optimality is defined in terms of various economic considerations. Then the efficiency of a particular unit is measured by its distance to the estimated frontier. The statistical problem can be viewed as the problem of estimating the support of a multivariate random variable, subject to some shape constraints, in multiple dimensions. These techniques are applied in thousands of papers in the economic and business literature. This "Guided Tour" reviews the development of various nonparametric approaches since the early work of Farrell. Remaining challenges and open issues in this challenging arena are also described.

When Bias Kills the Variance: Central Limit Theorems for DEA and FDH Efficiency Scores

Abstract: Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiencies of production units In applications, both efficiency scores for individual units as well as average efficiency scores are typically reported While several bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods have existed for making inference about mean efficiency levels This paper shows that standard central limit theorems do not apply in the case of means of DEA or FDH efficiency scores due to the bias of the individual scores, which is of larger order than either the variance or covariances among individual scores The main difficulty comes from the fact that such statistics depend on efficiency estimators evaluated at random points Here, new central limit theorems are developed for means of DEA and FDH scores, and their efficacy for inference about mean efficiency levels is examined via Monte Carlo experiments

The evolution of scale economies in US banking

Abstract: Summary Continued consolidation of the US banking industry and a general increase in the size of banks have prompted some policymakers to consider policies that discourage banks from getting larger, including explicit caps on bank size. However, limits on the size of banks could entail economic costs if they prevent banks from achieving economies of scale. This paper presents new estimates of returns to scale for US banks based on nonparametric, local-linear estimation of bank cost, revenue, and profit functions. We report estimates for both 2006 and 2015 to compare returns to scale some 7 years after the financial crisis and 5 years after enactment of the Dodd–Frank Act with returns to scale before the crisis. We find that a high percentage of banks faced increasing returns to scale in cost in both years, including most of the 10 largest bank holding companies. Also, while returns to scale in revenue and profit vary more across banks, we find evidence that the largest four banks operate under increasing returns to scale.

The Evolution of Scale Economies in U.S. Banking

Abstract: Continued consolidation of the U.S. banking industry and general increase in the size of banks has prompted some policymakers to consider policies to discourage banks from getting larger, including explicit caps on bank size. However, limits on the size of banks could entail economic costs if they prevent banks from achieving economies of scale. The extent of scale economies in banking remains unclear. This paper presents new estimates of returns to scale for U.S. banks based on nonparametric, local-linear estimation of bank cost, revenue and profit functions. We present estimates for both 2006 and 2014 to compare the extent of scale economies in banking some six years after the financial crisis and four years after enactment of the Dodd-Frank Act with scale economies prior to the crisis. We find that a high percentage of banks faced increasing returns to scale in cost in both years, including all of the 10 largest bank holding companies. Revenue and profit economies vary more across banks, though in both years nearly all banks could increase revenue and profit by becoming larger.

Assessing the effect of high performance computing capabilities on academic research output

Abstract: This paper uses nonparametric methods and some new results on hypothesis testing with nonparametric efficiency estimators and applies these to analyze the effect of locally available high performance computing (HPC) resources on universities’ efficiency in producing research and other outputs We find that locally available HPC resources enhance the technical efficiency of research output in Chemistry, Civil Engineering, Physics, and History, but not in Computer Science, Economics, nor English; we find mixed results for Biology Our research results provide a critical first step in a quantitative economic model for investments in HPC

Testing the "Separability" Condition in Two-Stage Nonparametric Models of Production

Abstract: Simar and Wilson (J Econometrics, 2007) provided a statistical model that can rationalize two-stage estimation of technical efficiency in nonparametric settings Two- stage estimation has been widely used, but requires a strong assumption: the second- stage environmental variables cannot affect the support of the input and output vari- ables in the first stage In this paper, we provide a fully nonparametric test of this assumption The test relies on new central limit theorem (CLT) results for uncondi- tional efficiency estimators developed by Kneip et al (Econometric Theory, 2015a) and new CLTs for conditional efficiency estimators developed in this paper The test can be implemented relying on either asymptotic normality of the test statistics or using bootstrap methods to obtain critical values Our simulation results indicate that our tests perform well both in terms of size and power We present a real-world empiri- cal example by updating the analysis performed by Aly et al (R E Stat, 1990) on US commercial banks; our tests easily reject the assumption required for two-stage estimation, calling into question results that appear in hundreds of papers that have been published in recent years

The Evolution of Scale Economies in U.S. Banking

Abstract: Continued consolidation of the U.S. banking industry and a general increase in the size of banks has prompted some policymakers to consider policies that discourage banks from getting larger, including explicit caps on bank size. However, limits on the size of banks could entail economic costs if they prevent banks from achieving economies of scale. This paper presents new estimates of returns to scale for U.S. banks based on nonparametric, local-linear estimation of bank cost, revenue and profit functions. We report estimates for both 2006 and 2015 to compare returns to scale some seven years after the financial crisis and five years after enactment of the Dodd-Frank Act with returns to scale before the crisis. We find that a high percentage of banks faced increasing returns to scale in cost in both years, including most of the 10 largest bank holding companies. And, while returns to scale in revenue and profit vary more across banks, we find evidence that the largest four banks operate under increasing returns to scale.