Model implied instrumental variables using MIIVsem: An R Package for Structural Equation Models
Kenneth Bollen and Zachary Fisher (UNC-Chapel Hill)
If our models were perfectly valid and our variables only came from normal distributions, the maximum likelihood and related estimators that dominate SEM software would be hard to beat. In reality, however, such structural and distributional assumptions are rarely if ever satisfied. This workshop will discuss more robust estimators that better represent real world conditions. Model Implied Instrumental Variable (MIIV) estimators are more robust to the approximate nature of our models and are asymptotically distribution free. In addition, they can test equation level fit so as to better localize model misspecification. The workshop will give an overview of the free R package MIIVsem. We will introduce the key ideas behind MIIV estimation; we will show how MIIVsem automates the selection of MIIVs, the estimation of coefficients and standard errors, and provides overidentification tests for equations. These and other features will be introduced and illustrated with a variety of empirical examples. We will provide instructions on downloading R and MIIVsem and will ask participants to bring their computers to the workshop so that they can run these empirical examples on their own machines during the last part of the workshop. No prior knowledge of R or MIIVsem is assumed.