Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging
Contribution of this paper lies in the formulation and estimation of a generalized model for stochastic frontier analysis (SFA) that nests virtually all forms used and includes some that have not been considered so far. The model is based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term. We use this general error structure framework for formal testing, to compare alternative specifications and to conduct model averaging. This allows us to deal with model specification uncertainty, which is one of the main unresolved issues in SFA, and to relax a number of potentially restrictive assumptions embedded within existing SF models. We also develop Bayesian inference methods that are less restrictive compared to the ones used so far and demonstrate feasible approximate alternatives based on maximum likelihood.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceSimilar Papers 제목 키워드 기반
Identifying the Frontier Structural Function and Bounding Mean Deviations
This paper analyzes a model in which an outcome variable equals a frontier function of inputs less a nonnegative unobserved deviation. If zero lies in the support of the deviation given inputs, then the frontier function…
validPRISM: Pushing the Frontier of Deep Think via Process Reward Model-Guided Inference
DEEPTHINK methods improve reasoning by generating, refining, and aggregating populations of candidate solutions, which enables strong performance on complex mathematical and scientific tasks. However, existing frameworks…
Accelerated Gradient Methods with Biased Gradient Estimates: Risk Sensitivity, High-Probability Guarantees, and Large Deviation Bounds
We study trade-offs between convergence rate and robustness to gradient errors in the context of first-order methods. Our focus is on generalized momentum methods (GMMs)--a broad class that includes Nesterov's accelerate…
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…
DiagnosticAdaptive debiased SGD in high-dimensional GLMs with streaming data
Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This paper introduces a novel approach to online infe…