Simultaneous Equation Models

Instructor(s):

  • Sandy Marquart-Pyatt, Michigan State University

This workshop will be offered in an online video format.

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.

Fee and Registration: This course is part of the second four-week session. Please see our fee chart on our Registration page for the cost of attending one (or both) four-week session(s). Participants who enroll in a four-week session may take as many courses (workshops and lectures) as desired during the session for which they are enrolled. Participants in the four-week sessions are also welcome to attend all of the lectures and discussions offered in the Blalock Lecture Series.

Tags: simultaneous equations, causal models

Course Sections

Section 1

Location: Online -- Video format,

Date(s): July 20 - August 14

Time: 10:00 AM - 12:00 PM

Instructor(s):

  • Sandy Marquart-Pyatt, Michigan State University