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多投入与多产出的非参数随机前沿模型AI 解读

Stochastic frontier models along the lines of Aigner et al. are widely used to benchmark firms’ performances in terms of efficiency. The models are typically fully parametric, with functional form specifications for the frontier as well as both the noise and the inefficiency processes. Studies such as Kumbhakar et al. have attempted to relax some of the restrictions in parametric models, but so far all such approaches are limited to a univariate response variable. Some…

动态非参数生产模型中的推断：Malmquist 指数的中心极限定理AI 解读

The Malmquist index gives a measure of productivity in dynamic settings and has been widely applied in empirical work. The index is typically estimated using envelopment estimators, particularly data envelopment analysis (DEA) estimators. Until now, inference about productivity change measured by Malmquist indices has been problematic, including both inference regarding productivity change experienced by particular firms as well as mean productivity change. This paper…

条件效率测度的中心极限定理与非参数两阶段生产模型的可分性检验AI 解读

In this paper, we demonstrate that standard central limit theorem (CLT) results do not hold for means of non‐parametric, conditional efficiency estimators, and we provide new CLTs that permit applied researchers to make valid inference about mean conditional efficiency or to compare mean efficiency across groups of producers. The new CLTs are used to develop a test of the restrictive ‘separability’ condition that is necessary for second‐stage regressions of efficiency…

美国银行规模经济的演变AI 解读

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…

非参数生产模型的假设检验AI 解读

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units,…

当偏差压倒方差：DEA 与 FDH 效率得分的中心极限定理AI 解读

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…

美国商业银行效率与生产率变化的稳健非参数分位数估计：1985—2004年AI 解读

This article uses a new nonparametric, unconditional, hyperbolic order-αquantile estimator to construct a hyperbolic version of the Malmquist index. Unlike traditional nonparametric efficiency estimators, the new estimator is both robust to data outliers and has a root-n convergence rate. We use this estimator to examine changes in the efficiency and productivity of U.S. banks between 1985 and 2004. We find that larger banks experienced larger efficiency and productivity…

非参数前沿模型中 DEA 估计量的渐近理论与一致自助法AI 解读

Nonparametric data envelopment analysis (DEA) estimators based on linear programming methods have been widely applied in analyses of productive efficiency. The distributions of these estimators remain unknown except in the simple case of one input and one output, and previous bootstrap methods proposed for inference have not been proved consistent, making inference doubtful. This paper derives the asymptotic distribution of DEA estimators under variable returns to scale.…

效率分析的非参数无条件分位数估计：美联储支票处理业务应用AI 解读

This paper examines the technical efficiency of U.S. Federal Reserve check processing offices over 1980–2003. We extend results from Park et al. (2000) and Daouia and Simar (2007) to develop an unconditional, hyperbolic, α-quantile estimator of efficiency. Our new estimator is fully non-parametric and robust with respect to outliers; when used to estimate distance to quantiles lying close to the full frontier, it is strongly consistent and converges at rate root-n, thus…

生产过程两阶段半参数模型的估计与推断AI 解读

A plethora of papers have used multi-stage estimation procedures where nonparametric estimates of productive efficiency are obtained in the first stage and then regressed on environmental variables in a subsequent stage in attempts to account for exogenous factors that might affect firms’ performance. None of these papers have described a coherent data-generating process (DGP). Moreover, conventional approaches to inference employed in these papers are invalid due to…

美国医院行业规模报酬的非参数分析AI 解读

Abstract This paper presents new estimates of scale economies for US hospitals. We show that the common translog specification of hospital costs is a misspecification, and employ nonparametric, local linear estimation with both continuous and discrete covariates. A bootstrap method is used to provide inferences regarding ray scale economies and expansion path scale economies for a large sample of hospitals covering 1984–1996. We find evidence of changes in the structure of…

多产出确定性非参数前沿模型中的异常值检测AI 解读

This article provides a statistical methodology for identifying outliers in production data with multiple inputs and outputs used in deterministic nonparametric frontier models. The methodology is useful in identifying observations that may contain some form of measurement error and, thus, merit closer scrutiny. When data checking is costly, the methodology may be used to rank observations in terms of their dissimilarity to other observations in the data, suggesting a…

一般线性与 SUR 模型中自助法和常规标准误的偏差AI 解读

When estimating the seemingly unrelated regression (SUR) model in small samples, the bootstrap feasible generalized least-squares (FGLS) covariance estimator has been widely advocated as less biased than the conventional FGLS covariance estimator obtained by evaluating the asymptotic covariance matrix. Assuming multivariate normal errors and an unbiased estimator of the error covariance, Eaton proves that the conventional estimator is biased downward for a general SUR model.…