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A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection: empirical likelihood approach for conditional estimating equations

A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection: empirical likelihood approach for conditional estimating equations
A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection: empirical likelihood approach for conditional estimating equations
The estimation and inference for conditional estimating equations models with endogenous selection, are considered. The approach takes into account possible endogenous selection which may lead to a selection bias. It can be used for a wide range of statistical models not covered by the model-based sampling theory. Endogeneity can be either part of the selection or within the covariates. It is particularly well suited for models with unknown heteroscedasticity, uncontrolled confounders and measurement errors. It will not be necessary to model the relationship between the endogenous covariates and the instrumental variables, which offer major advantages over two-stage least-squares. The approach proposed has the advantage of being based on a fixed number of constraints determined by the size of the parameter.
Conditional estimating equations, Endogenenous covariates, Endogenenous stratification, Transformation model, Two-stage least-squares
2452-3062
151-163
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Patilea, Valentin
a96956f4-78aa-4d4e-b369-3bd5c5ce7140
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Patilea, Valentin
a96956f4-78aa-4d4e-b369-3bd5c5ce7140

Berger, Yves and Patilea, Valentin (2022) A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection: empirical likelihood approach for conditional estimating equations. Econometrics and Statistics, 24, 151-163. (doi:10.1016/j.ecosta.2021.12.004).

Record type: Article

Abstract

The estimation and inference for conditional estimating equations models with endogenous selection, are considered. The approach takes into account possible endogenous selection which may lead to a selection bias. It can be used for a wide range of statistical models not covered by the model-based sampling theory. Endogeneity can be either part of the selection or within the covariates. It is particularly well suited for models with unknown heteroscedasticity, uncontrolled confounders and measurement errors. It will not be necessary to model the relationship between the endogenous covariates and the instrumental variables, which offer major advantages over two-stage least-squares. The approach proposed has the advantage of being based on a fixed number of constraints determined by the size of the parameter.

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Berger_Patilea_2021 - Accepted Manuscript
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More information

Accepted/In Press date: 4 December 2021
e-pub ahead of print date: 23 December 2021
Published date: 5 October 2022
Additional Information: Funding Information: This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI UEFISCDI, project number PN-III-P4-ID-PCE-2020-1112, within PNCDI III. Publisher Copyright: © 2021 EcoSta Econometrics and Statistics
Keywords: Conditional estimating equations, Endogenenous covariates, Endogenenous stratification, Transformation model, Two-stage least-squares

Identifiers

Local EPrints ID: 452886
URI: http://eprints.soton.ac.uk/id/eprint/452886
ISSN: 2452-3062
PURE UUID: 2a9a81bb-c97b-4ea7-afbe-5e0730d00ca7
ORCID for Yves Berger: ORCID iD orcid.org/0000-0002-9128-5384

Catalogue record

Date deposited: 06 Jan 2022 17:40
Last modified: 10 Jan 2023 05:01

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Contributors

Author: Yves Berger ORCID iD
Author: Valentin Patilea

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