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Optimal Instrument Selection Using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators

Title: Optimal Instrument Selection Using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators
Language: English
Authors: Teague R. Henry; Zachary F. Fisher; Kenneth A. Bollen
Source: Structural Equation Modeling: A Multidisciplinary Journal. 2024 31(6):965-982.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 18
Publication Date: 2024
Sponsoring Agency: National Institute of Mental Health (NIMH) (DHHS/NIH)
Contract Number: 1R21MH11957201
Document Type: Journal Articles; Reports - Research
Descriptors: Bayesian Statistics; Least Squares Statistics; Structural Equation Models; Equations (Mathematics); Computation; Research Problems; Identification; Simulation; Measurement
DOI: 10.1080/10705511.2024.2343926
ISSN: 1070-5511; 1532-8007
Abstract: Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, noniterative estimator for latent variable models. Associated with this estimator are equation-specific tests of model misspecification. One issue with equation-specific tests is that they lack specificity, in that they indicate that some instruments are problematic without revealing which specific ones. Instruments that are poor predictors of their target variables ("weak instruments") is a second potential problem. We propose a novel extension to detect instrument-specific tests of misspecification and weak instruments. We term this the Model-Implied Instrumental Variable Two-Stage Bayesian Model Averaging (MIIV-2SBMA) estimator. We evaluate the performance of MIIV-2SBMA against MIIV-2SLS in a simulation study and show that it has comparable performance in terms of parameter estimation. Additionally, our instrument-specific overidentification tests developed within the MIIV-2SBMA framework show increased power to detect specific problematic and weak instruments. Finally, we demonstrate MIIV-2SBMA using an empirical example.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1448357
Database: ERIC