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A weighted average limited information maximum likelihood estimator

Qasim, Muhammad LU (2024) In Statistical Papers 65(5). p.2641-2666
Abstract

In this article, a Stein-type weighted limited information maximum likelihood (LIML) estimator is proposed. It is based on a weighted average of the ordinary least squares (OLS) and LIML estimators, with weights inversely proportional to the Hausman test statistic. The asymptotic distribution of the proposed estimator is derived by means of local-to-exogenous asymptotic theory. In addition, the asymptotic risk of the Stein-type LIML estimator is calculated, and it is shown that the risk is strictly smaller than the risk of the LIML under certain conditions. A Monte Carlo simulation and an empirical application of a green patent dataset from Nordic countries are used to demonstrate the superiority of the Stein-type LIML estimator to the... (More)

In this article, a Stein-type weighted limited information maximum likelihood (LIML) estimator is proposed. It is based on a weighted average of the ordinary least squares (OLS) and LIML estimators, with weights inversely proportional to the Hausman test statistic. The asymptotic distribution of the proposed estimator is derived by means of local-to-exogenous asymptotic theory. In addition, the asymptotic risk of the Stein-type LIML estimator is calculated, and it is shown that the risk is strictly smaller than the risk of the LIML under certain conditions. A Monte Carlo simulation and an empirical application of a green patent dataset from Nordic countries are used to demonstrate the superiority of the Stein-type LIML estimator to the OLS, two-stage least squares, LIML and combined estimators when the number of instruments is large.

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Please use this url to cite or link to this publication:
author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
2SLS, Endogeneity, Instrumental variables, LIML, Many weak instruments, Shrinkage estimator, Stein estimation, C13, C26
in
Statistical Papers
volume
65
issue
5
pages
26 pages
publisher
Springer
external identifiers
  • scopus:85173791738
ISSN
0932-5026
DOI
10.1007/s00362-023-01485-2
language
English
LU publication?
no
additional info
Publisher Copyright: © The Author(s) 2023.
id
a4b842c4-0b33-4a43-a1f9-602e952df7a3
date added to LUP
2025-01-20 12:36:09
date last changed
2025-04-04 14:02:40
@article{a4b842c4-0b33-4a43-a1f9-602e952df7a3,
  abstract     = {{<p>In this article, a Stein-type weighted limited information maximum likelihood (LIML) estimator is proposed. It is based on a weighted average of the ordinary least squares (OLS) and LIML estimators, with weights inversely proportional to the Hausman test statistic. The asymptotic distribution of the proposed estimator is derived by means of local-to-exogenous asymptotic theory. In addition, the asymptotic risk of the Stein-type LIML estimator is calculated, and it is shown that the risk is strictly smaller than the risk of the LIML under certain conditions. A Monte Carlo simulation and an empirical application of a green patent dataset from Nordic countries are used to demonstrate the superiority of the Stein-type LIML estimator to the OLS, two-stage least squares, LIML and combined estimators when the number of instruments is large.</p>}},
  author       = {{Qasim, Muhammad}},
  issn         = {{0932-5026}},
  keywords     = {{2SLS; Endogeneity; Instrumental variables; LIML; Many weak instruments; Shrinkage estimator; Stein estimation; C13; C26}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{2641--2666}},
  publisher    = {{Springer}},
  series       = {{Statistical Papers}},
  title        = {{A weighted average limited information maximum likelihood estimator}},
  url          = {{http://dx.doi.org/10.1007/s00362-023-01485-2}},
  doi          = {{10.1007/s00362-023-01485-2}},
  volume       = {{65}},
  year         = {{2024}},
}