Model-implied instrumental variable-generalized method of moments (MIIV-GMM) estimators for latent variable models.

ABSTRACT: The common maximum likelihood (ML) estimator for structural equation models (SEMs) has optimal asymptotic properties under ideal conditions (e.g., correct structure, no excess kurtosis, etc.) that are rarely met in practice. This paper proposes model-implied instrumental variable - generalized method of moments (MIIV-GMM) estimators for latent variable SEMs that are more robust than ML to violations of both the model structure and distributional assumptions. Under less demanding assumptions, the MIIV-GMM estimators are consistent, asymptotically unbiased, asymptotically normal, and have an asymptotic covariance matrix. They are "distribution-free," robust to heteroscedasticity, and have overidentification goodness-of-fit J-tests with asymptotic chi-square distributions. In addition, MIIV-G

SUBMITTER: Bollen KA 

PROVIDER: S-EPMC6705389 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature