MGTECON 606: Microeconomic Theory for Non-Economist PhDs
This course will be a first quarter PhD course in microeconomic theory, aimed at PhD students who do not plan to become professional economists. Relative to a course geared to economics PhDs the class will differ in two important ways. First, there will be almost no emphasis on proofs. Second, the topics covered will be broader than the standard set covered in say
Econ 202.
Last offered: Autumn 2024
| Units: 3
MGTECON 607: Methods for Applied Econometrics
The course continues a sequence in modern econometric methods for empirical work in economics. Topics may include: identification and misspecification, asymptotic theory of extremum estimation, generalized method of moments, discrete choice models, nonparametric regression, panel data, and large-scale statistical inference.
Terms: Spr
| Units: 4
Instructors:
Chen, K. (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.
Last offered: Spring 2025
| Units: 3
MGTECON 610: Macroeconomics
This course covers various topics in macroeconomics and is designed to expose students to macroeconomic methods, classic papers in the field, and the latest research at the frontier. The current focus is on economic growth. Using theoretical and empirical tools, we consider questions like: How do we understand long-run growth in per capita income? Why are some countries so much richer than others? Other topics include misallocation as a source of TFP differences, the direction of technical change, growth and the environment, the rise in health spending, patenting, and international trade. This course satisfies the GSB PhD macro requirement.
Terms: Aut
| Units: 4
Instructors:
Jones, C. (PI)
;
Klenow, P. (SI)
MGTECON 612: Advanced Macroeconomics II
The course is part of the second year PhD Macro sequence at Stanford. We will cover both standard topics and recent advances in the field. We emphasize solution methods for models in both continuous and discrete time. The goal is to make you aware of the core body of research in monetary economics, and to inspire and prepare you to write your own research papers.
Terms: Win
| Units: 4
Instructors:
Auclert, A. (PI)
;
Di Tella, S. (PI)
MGTECON 614: Emerging Topics in Econometrics
In this course we will discuss emerging topics in econometrics. Possible topics include reanalyses of widely used econometric methods in more challenging settings (e.g., negative weights of two-way-fixed-effect regression), partial identification, design of randomized experiments, empirical Bayes methods, shrinkage estimators and regularized regression (e.g., LASSO), matrix completion methods for panel and network data, adversarial methods, interference and peer effects, randomization inference, regression adjustment techniques in experiments, sensitivity analysis, and decision making under ambiguity. Students are expected to read recent research papers, write short summaries and discussions of them, and work on a final research project that has the potential to be developed into a full-blown paper.
Terms: Spr
| Units: 3
Instructors:
Lei, L. (PI)
MGTECON 616: Topics in Microeconomic Theory
This course covers foundational topics in microeconomic theory and is suitable for students who have completed the first year of their PhD studies and have taken a game theory or another advanced theory course. Sample topics include the notions of interactive knowledge and beliefs of economic agents, epistemics of solution concepts, strategies in continuous time and (adversarial) prediction testing. The course combines lectures with workshop sessions where students confront related research-type problems. Students are asked to present their original solutions to the class and are expected to participate in class discussions.
Last offered: Winter 2025
| Units: 3
MGTECON 617: Heterogeneity in Macroeconomics and Finance
The goal of this course is to introduce students to frontier research in quantitative macroeconomics and finance with heterogeneous agents. We study models with imperfect financial markets and/or search frictions. We emphasize theory and numerical methods as well as tools to confront model predictions with both micro and macro data. Potential applications cover a wide range of topics in household finance, corporate finance and firm dynamics, asset pricing, housing and labor markets, business cycles and growth.
Terms: Win
| Units: 3
Instructors:
Schneider, M. (PI)
;
Tonetti, C. (PI)
MGTECON 618: Social Insurance and Urban Economics
The course covers various topics relating to social insurance and urban economics. The first half of the course covers the rationale for government interventions into private insurance markets, adverse selection, social insurance design and the intersection between social insurance and intra-family insurance. The second half of the course covers topics in urban economics, such as spatial equilibrium, placed-based policies, and housing policy.
Terms: Win
| Units: 4
Instructors:
Allende Santa Cruz, C. (PI)
;
Persson, P. (SI)
MGTECON 621: Topics in Economics and Computation
In our digital economy, it can be difficult to understand markets without understanding the algorithms that underlie them. Similarly, it can be difficult to design effective algorithms without taking into account the preferences and incentives of the humans they serve. Recognizing that, this course covers topics at the intersection of economics and computer science. The primary topic this year is the theory of recommender systems: how to help consumers find products that they value. We will explore these systems through the lens of mechanism design, econometrics, and bounded rationality. Secondary topics may include algorithmic mechanism design, preference elicitation, privacy, algorithmic collusion, and AI alignment. Students will be introduced to relevant tools from computational complexity and statistical learning theory. Prerequisites: PhD-level course in microeconomic theory.
Terms: Win
| Units: 3
Instructors:
Camara, M. (PI)