存在内生性的概率模型的斯坦因型控制函数最大似然估计器

IF 2 Q2 ECONOMICS

Econometrics and Statistics Pub Date : 2023-12-10 DOI:10.1016/j.ecosta.2023.12.001

Muhammad Qasim, Kristofer Månsson, Pär Sjölander, B. M. Golam Kibria

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引用次数: 0

摘要

针对存在内生性的 probit 模型，提出了一种 Stein 型控制函数最大似然（CFML）估计方法。这种新型估计器结合了 probit 最大似然估计器和 CFML 估计器。推导了新估计器的渐近分布和风险函数。结果表明，在收缩参数的某些条件下，新估计器的渐近风险严格小于 CFML 的风险。蒙特卡罗模拟说明了该方法在有限样本中的优越性。该方法还被用于分析管理激励对外汇衍生品使用的影响。

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Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity

A Stein-type control function maximum likelihood (CFML) estimator is suggested for the probit model in the presence of endogeneity. This novel estimator combines the probit maximum likelihood and CFML estimators. The asymptotic distribution and risk function for the new estimator is derived. It is demonstrated that, subject to certain conditions of the shrinkage parameter, the asymptotic risk of the new estimator is strictly smaller than the risk of the CFML. Monte Carlo simulations illustrate the method's superiority in finite samples. The method is also applied to analyze the impact of managerial incentives on the use of foreign-exchange derivatives.

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来源期刊

Econometrics and Statistics ECONOMICS-

CiteScore

3.10

自引率

10.50%

发文量

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.