151 - 160 of 173 results for: ECON

Topics course covering a variety of game theory topics with emphasis on market design, such as matching theory and auction theory. Final paper required. Prerequisites: ECON 285 or equivalent. ECON 283 recommended.

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, and computation.

Synthesis of research on social and economic networks by sociologists, economists, computer scientists, physicists, and mathematicians, with an emphasis on modeling. Includes methods for describing and measuring networks, empirical observations about network structure, models of random and strategic network formation, as well as analyses of contagion, diffusion, learning, peer influence, games played on networks, and networked markets.

This is an advanced course on quantitative methods for empirical research. Students are expected to have taken a course in linear models before. In this course I will discuss modern econometric methods for nonlinear models, including maximum likelihood and generalized method of moments. The emphasis will be on how these methods are used in sophisticated empirical work in social sciences. Special topics include discrete choice models and methods for estimating treatment effects.

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. Prerequisite: Prior coursework in basic observational study methods for causal inference, including instrumental variables, fixed effects modeling, regression discontinuity designs, etc. Students should be comfortable reading and engaging with empirical research in economics and related fields. This is crosslisted with MGTECON 634.

Continuous-time methods can, in many cases, lead to more powerful models to understand economic phenomena. The Black-Scholes option-pricing formula is significantly more tractable than discrete- time methods of option pricing based on binomial trees. There is an established tradition in continuous-time asset pricing, and there is increasing use of these methods in other fields, such as game theory, contract theory, market microstructure and macroeconomics. The goal of this class is to explore some of the old classic research as well as new economic models, and to discover areas of economics where continuous-time methods can help. The intention is to give graduate students a tool, which they can use to gain comparative advantage in their research, when they see appropriate. With this goal in mind, 25% of the class will focus on mathematics, but with economically relevant examples to illustrate the mathematical results. Up to one half of the class will cover established models, and the rest will focus on new papers. If students have their own work that uses continuous time, we can take a look at that as well. Coursework will include biweekly problem sets and a take-home final exam. There will also be room for short student presentations (related to homework assignments, economic papers, or definitions and results related to specific math concepts).

This course examines how advances in AI are transforming the economy and reshaping the frontier of entrepreneurship. In Spring 2026, the course will emphasize venture creation: students will explore how large language models and other AI tools enable small teams to build products and companies with unprecedented speed and scale. Each week features guest speakers who are leaders in AI, business, economics, and industry, alongside discussion of cutting-edge research and its practical implications. Working in interdisciplinary teams, students will develop, prototype, and refine an AI-enabled product or startup concept, culminating in a final presentation of a working prototype and venture strategy. Designed primarily for graduate students in economics, business, computer science, and related fields. Admission is by application only. To learn more and apply, please visit https://digitaleconomy.stanford.edu/about/education/the-ai-awakening-implications-for-the-economy-and-society/; applications close at 5pm PT on March 16, 2026, with priority given to those received by March 9.

Students obtain employment in a relevant research or industrial activity to enhance their professional experience consistent with their degree programs. At the start of the quarter, students must submit a one page statement showing the relevance of the employment to the degree program along with an offer letter. Submit this documentation to the Econ professor, who has agreed to the student enrolling in their Econ 299 section. At the end of the quarter, a three page final report must be supplied documenting work done and relevance to degree program. May be repeated for credit.

Restricted to Economics Ph.D. students. Students present current research. May be repeated for credit.