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

Qasim, Muhammad LU ; Månsson, Kristofer ; Sjölander, Pär and Kibria, B. M.Golam (2023) In Econometrics and Statistics
Abstract

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.

Please use this url to cite or link to this publication:
author
; ; and
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Control function, Endogeneity, Instrumental variable, Model averaging, Probit Stein estimator
in
Econometrics and Statistics
publisher
Elsevier
external identifiers
  • scopus:85181133971
ISSN
2452-3062
DOI
10.1016/j.ecosta.2023.12.001
language
English
LU publication?
no
additional info
Publisher Copyright: © 2023 The Author(s)
id
0a1f202d-b70d-4de3-a413-a55a4a892ee4
date added to LUP
2025-02-26 20:05:19
date last changed
2025-04-04 14:08:41
@article{0a1f202d-b70d-4de3-a413-a55a4a892ee4,
  abstract     = {{<p>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.</p>}},
  author       = {{Qasim, Muhammad and Månsson, Kristofer and Sjölander, Pär and Kibria, B. M.Golam}},
  issn         = {{2452-3062}},
  keywords     = {{Control function; Endogeneity; Instrumental variable; Model averaging; Probit Stein estimator}},
  language     = {{eng}},
  month        = {{12}},
  publisher    = {{Elsevier}},
  series       = {{Econometrics and Statistics}},
  title        = {{Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity}},
  url          = {{http://dx.doi.org/10.1016/j.ecosta.2023.12.001}},
  doi          = {{10.1016/j.ecosta.2023.12.001}},
  year         = {{2023}},
}