Economics Ph.D. Program: Graduate Courses


Overview | Requirements | Fellowships | Timetable | Courses


A complete description of the Course requirements for the Ph.D. program is provided on the Requirements webpage. The list provided here will give you a quick view of the first year, and links will take you to current schedules and the full list of all graduate courses that are offered from time to time.

First Year Courses

During the first year, students typically take five core courses (ECO 471, 472, 475, 476, 481) as well as the econometrics sequence (ECO 483, 484, 485). These requirements provide the foundation of modern economic analysis upon which the whole program builds. Course numbers are linked to descriptions below.

Fall

Spring


Course Descriptions

471. Modern Value Theory I
The foundation of modern microeconomic analysis, including consideration of consumer behavior, the theory of the firm, equilibrium under alternative market structures, and welfare implications.
472. Modern Value Theory II
Introduction to general equilibrium analysis, including modern treatment of existence, stability, and comparative statics properties; elements of capital theory.
475. Macroeconomics I
Reviews the main empirical regularities that characterize economic growth and business fluctuations in market economies. Discusses various theoretical models of the business cycle, as well as the macroeconomic impact of fiscal and monetary policy.
476. Macroeconomics II
This course continues on with the themes developed in 475: business cycles, economic growth, fiscal and monetary policies. More emphasis is placed on the tools required to do modern macroeconomics: dynamic programming, difference equations, Markov chains, etc. Computational techniques such as linear quadratic and discrete state space dynamic programming, the Coleman algorithm, and parameterized expectations are taught. (No prior knowledge of these techniques is assumed).
481. Mathematical Economics I
This course covers the use of optimization theory in economic analysis. The topics covered include finite-dimensional optimization (unconstrained optimization, Lagrange's Theorem, the Kuhn-Tucker Theorem), the role of convexity in optimization, parametric continuity of solutions to optimization problems, and finite- and infinite-horizon dynamic programming.
483. Introduction to Mathematical Statistics
Credit-two hours
Elements of probability theory and statistics, as employed in the econometrics sequence ECO
 
484. Introduction to Econometrics
(Same as APS 514)
Prerequisite: ECO 483 or permission of department.
Credit-two hours
Estimation and hypothesis testing in the standard linear model. Linear restrictions; dummy variables; multicollinearity; weighted least squares; specification error.
485. Elements of Econometrics
(Same as APS 515)
Prerequisite: ECO 484.
Extensions of the general linear model to handle serial correlation, heteroskedasticity, simultaneity. Maximum likelihood estimation and testing. Diagnostic checking of estimated models. Problems in the analysis of individual unit data-qualitative dependent variables and sample self-selectivity.

List of all Graduate courses with descriptions