基于经验变换的正态/伽马和稳定/伽马随机前沿模型的规格检验

IF 2 Q2 ECONOMICS
Christos K. Papadimitriou, Simos G. Meintanis, Bernardo B. Andrade, Mike G. Tsionas
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引用次数: 0

摘要

提出了随机前沿模型(SFM)中组成误差项分布的拟合优度检验。重点是正态/伽马 SFM 和重尾稳定/伽马 SFM 的情况。在前一种情况下,使用矩生成函数作为工具,而在后一种情况下,则使用误差项的特征函数。在这两种情况下,我们的检验统计量都被表述为适当标准化数据的加权积分。新的正态/伽马检验是一致的,而且与基于矩的检验有内在联系。蒙特卡洛模拟研究了这两种检验的重采样版本的有限样本行为,同时还包括几个实际数据应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Specification tests for normal/gamma and stable/gamma stochastic frontier models based on empirical transforms
Goodness–of–fit tests for the distribution of the composed error term in a Stochastic Frontier Model (SFM) are suggested. The focus is on the case of a normal/gamma SFM and the heavy–tailed stable/gamma SFM. In the first case the moment generating function is used as tool while in the latter case the characteristic function of the error term is employed. In both cases our test statistics are formulated as weighted integrals of properly standardized data. The new normal/gamma test is consistent, and is shown to have an intrinsic relation to moment–based tests. The finite–sample behavior of resampling versions of both tests is investigated by Monte Carlo simulation, while several real–data applications are also included.
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
84
期刊介绍: Econometrics and Statistics is the official journal of the networks Computational and Financial Econometrics and Computational and Methodological Statistics. It publishes research papers in all aspects of econometrics and statistics and comprises of the two sections Part A: Econometrics and Part B: Statistics.
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