Nonparametric Tests of Tail Behavior in Stochastic Frontier Models
This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature.
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticSimilar Papers 제목 키워드 기반
An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functions
We present a novel family of nonparametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure. We developed these tests, which r…
A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules
The Generative Adversarial Network (GAN) was recently introduced in the literature as a novel machine learning method for training generative models. It has many applications in statistics such as nonparametric clusterin…
ClusteringGenerative Adversarial NetworkImage GenerationNonparametric ClusteringDeep Nonparametric Conditional Independence Tests for Images
Conditional independence tests (CITs) test for conditional dependence between random variables. As existing CITs are limited in their applicability to complex, high-dimensional variables such as images, we introduce deep…
Transfer LearningNonparametric Tests of Conditional Independence for Time Series
We propose consistent nonparametric tests of conditional independence for time series data. Our methods are motivated from the difference between joint conditional cumulative distribution function (CDF) and the product o…
Time SeriesTime Series AnalysisBootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity
Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivariate and non-Euclidean data, contemporary…
Two-sample testingVocal Bursts Valence Prediction