Simultaneous Equation Models

Instructor(s):

• Sandy Marquart-Pyatt, Sociology, Utah State University

This course centers on simultaneous equation models -- models of more than one equation, to account for more than one dependent variable -- formerly called "causal models." The workshop will focus on linear models of the standard econometric type, continuing on to nonlinear models, models with discrete dependent variables, and models with measurement error ("covariance structure models") as time permits. The course will cover the nature of simultaneous equation models, their parameters, and the "effects" they imply; the assumptions under which one would customarily analyze them; the identification problem and criteria for identifiability; and simultaneous equations estimators such as two- and three-stage least squares, and limited- and full-information maximum likelihood. Students taking this course should have previously taken the Regression Analysis II: Linear Models workshop and the Mathematics for Social Scientists II lecture series or their equivalents. The level of discourse is approximately that of Greene's Econometric Analysis.

Dates:  July 20-August 14 

Fee:  consult the fee structure