Half day short course (Monday, July 19; 9:00AM-12:30PM US Eastern)
If our models were perfectly valid and our variables 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. The Model Implied Instrumental Variable, Two Stage Least Squares (MIIV-2SLS) estimator is 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-2SLS 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 in advance of the workshop so that those participants who would like to practice with the program can do so. No prior knowledge of R or MIIVsem is assumed.