MGTECON 327: Business and Public Policy Perspectives on U.S. Inequality
This class will analyze the growth in inequality in the US over the last several decades and how that trend is likely to continue or change in the future. We will ask if and how public policy can affect inequality. We will also focus on business's role  what are the responsibilities of private sector companies, how does inequality affect them, and how should the growth in inequality affect their strategies? We will look at inequality in income, some of its potential sources, and its effects in other areas. Specifically, we will look at education, housing, the social safety net, migration, and the job market. The class will be very interactive and will be based on readings drawn from academic research, case studies, news, and opinion readings. We will also have guest speakers from industry, government, and nonprofits. The class will be cotaught by a GSB labor economist and an advisor to policy makers with decades of business experience.
Units: 3

Grading: GSB Letter Graded
Instructors:
Oyer, P. (PI)
;
Mendonca, L. (SI)
MGTECON 602: Auctions, Bargaining, and Pricing
This course covers mostly auction theory, bargaining theory and related parts of the literature on pricing. Key classic papers covered in the course are Myerson and Satterthwaite on dynamic bargaining, Myerson on optimal auctions, and Milgrom and Weber's classic work, the Coase Conjecture results. We also cover a few more recent developments related to these topics, including dynamic signaling and screening. In some years we also cover topics in matching theory.
Units: 4

Grading: GSB Student Option LTR/PF
Instructors:
Skrzypacz, A. (PI)
MGTECON 605: Econometric Methods III
This course completes the firstyear sequence in econometrics. It develops nonparametric, semiparametric and nonlinear parametric models in detail, as well as optimization methods used to estimate nonlinear models. The instructor will discuss identification issues, the statistical properties of these estimators, and how they are used in practice. Depending on student and instructor interest, we will consider advanced topics and applications, including: machine learning, simulation methods and Bayesian estimators.
Units: 3

Grading: GSB Letter Graded
Instructors:
Reiss, P. (PI)
MGTECON 608: Multiperson Decision Theory
Students and faculty review and present recent research papers on basic theories and economic applications of decision theory, game theory and mechanism design. Applications include market design and analyses of incentives and strategic behavior in markets, and selected topics such as auctions, bargaining, contracting, signaling, and computation.
Units: 3

Grading: GSB Pass/Fail
Instructors:
Wilson, R. (PI)
MGTECON 632: Topics in Continuous Time Dynamics
This seminarstyle course studies a selection of microeconomic models in dynamic settings, and explores the use of continuoustime methods to solve them. Topics to be covered include experimentation games, social learning, principalagent problems, career concerns/marketagent models, security design and strategic trading. For every topic discussed, the class introduces gradually the set of relevant mathematical tools: dynamic programming and HamiltonJacobiBellman equations, Pontryagin's maximum principle, EulerLagrange equations, Brownian and Poisson processes, Bayesian inference and linear filtering, change of measure, martingale representation, Malliavin derivatives, stochastic maximum principle, expansions of filtrations. The course emphasizes highlevel intuition rather than mathematical rigor. It is targeted at those who seek to become familiar with the literature on continuoustime dynamics and want to understand the functioning of these models, either by general interest or to apply these techniques.
Units: 3

Grading: GSB Student Option LTR/PF
Instructors:
Lambert, N. (PI)
MGTECON 634: Machine Learning and Causal Inference
This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. This course will review when and how machine learning methods can be used for causal inference, and it will also review recent modifications and extensions to standard methods to adapt them to causal inference and provide statistical theory for hypothesis testing. We consider causal inference methods based on randomized experiments as well as observational studies, including methods such as instrumental variables and those based on longitudinal data. We consider the estimation of average treatment effects as well as personalized policies. Lectures will focus on theoretical developments, while classwork will consist primarily of empirical applications of the methods. Prerequisites: graduate level coursework in at least one of statistics, econometrics, or machine learning. Students without prior exposure to causal inference will need to do additional background reading in the early weeks of the course.
Units: 3

Grading: GSB Student Option LTR/PF
Instructors:
Athey, S. (PI)
;
Wager, S. (SI)
MGTECON 652: Personnel Economics
This seminar will examine applications of labor economics to business issues and firms' practices. Material will include both theoretical and empirical work, and the syllabus will range from classics in Personnel Economics to current (unpublished) research. Some of the topics to be covered include, but are not limited to, compensation practices, assignment of decision rights, organizational structure, attracting, retaining, and displacing employees, and workplace practices (such as teambased organization, profit sharing, etc.)
Units: 3

Grading: GSB Student Option LTR/PF
MGTECON 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MKTG 691, OB 691, OIT 691, POLECON 691, STRAMGT 691)
This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.
Units: 115

Repeatable for credit

Grading: GSB Pass/Fail
Instructors:
Akbarpour, M. (PI)
;
Athey, S. (PI)
;
Benkard, L. (PI)
...
more instructors for MGTECON 691 »
Instructors:
Akbarpour, M. (PI)
;
Athey, S. (PI)
;
Benkard, L. (PI)
;
Bulow, J. (PI)
;
Di Tella, S. (PI)
;
Diamond, R. (PI)
;
Feinberg, Y. (PI)
;
Imbens, G. (PI)
;
Jones, C. (PI)
;
Kreps, D. (PI)
;
Lambert, N. (PI)
;
Lazear, E. (PI)
;
Ostrovsky, M. (PI)
;
Oyer, P. (PI)
;
Reiss, P. (PI)
;
Saloner, G. (PI)
;
Sannikov, Y. (PI)
;
Shaw, K. (PI)
;
Skrzypacz, A. (PI)
;
Somaini, P. (PI)
;
Sugaya, T. (PI)
;
Yurukoglu, A. (PI)
MGTECON 692: PhD Dissertation Research (ACCT 692, FINANCE 692, GSBGEN 692, HRMGT 692, MKTG 692, OB 692, OIT 692, POLECON 692, STRAMGT 692)
This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.
Units: 115

Repeatable for credit

Grading: GSB Pass/Fail
Instructors:
Akbarpour, M. (PI)
;
Athey, S. (PI)
;
Benkard, L. (PI)
...
more instructors for MGTECON 692 »
Instructors:
Akbarpour, M. (PI)
;
Athey, S. (PI)
;
Benkard, L. (PI)
;
Bulow, J. (PI)
;
Di Tella, S. (PI)
;
Diamond, R. (PI)
;
Feinberg, Y. (PI)
;
Imbens, G. (PI)
;
Jones, C. (PI)
;
Kreps, D. (PI)
;
Lambert, N. (PI)
;
Lazear, E. (PI)
;
Ostrovsky, M. (PI)
;
Oyer, P. (PI)
;
Reiss, P. (PI)
;
Saloner, G. (PI)
;
Sannikov, Y. (PI)
;
Shaw, K. (PI)
;
Skrzypacz, A. (PI)
;
Somaini, P. (PI)
;
Sugaya, T. (PI)
;
Yurukoglu, A. (PI)
MGTECON 698: Doctoral Practicum in Teaching
Doctoral Practicum in Teaching
Units: 1

Repeatable for credit

Grading: GSB Pass/Fail
Instructors:
Akbarpour, M. (PI)
;
Athey, S. (PI)
;
Benkard, L. (PI)
...
more instructors for MGTECON 698 »
Instructors:
Akbarpour, M. (PI)
;
Athey, S. (PI)
;
Benkard, L. (PI)
;
Bulow, J. (PI)
;
Di Tella, S. (PI)
;
Diamond, R. (PI)
;
Feinberg, Y. (PI)
;
Imbens, G. (PI)
;
Jones, C. (PI)
;
Kreps, D. (PI)
;
Lambert, N. (PI)
;
Lazear, E. (PI)
;
Ostrovsky, M. (PI)
;
Oyer, P. (PI)
;
Reiss, P. (PI)
;
Saloner, G. (PI)
;
Sannikov, Y. (PI)
;
Shaw, K. (PI)
;
Skrzypacz, A. (PI)
;
Somaini, P. (PI)
;
Sugaya, T. (PI)
;
Yurukoglu, A. (PI)