2014-2015 2015-2016 2016-2017 2017-2018 2018-2019
Browse
by subject...
    Schedule
view...
 

121 - 130 of 147 results for: ECON

ECON 282: Contracts, Information, and Incentives

Basic theories and recent developments in mechanism design and the theory of contracts. Topics include: hidden characteristics and hidden action models with one and many agents, design of mechanisms and markets with limited communication, long-term relationships under commitment and under renegotiation, property rights and theories of the firm.
Terms: Win | Units: 2-5 | Grading: Letter or Credit/No Credit
Instructors: Segal, I. (PI)

ECON 283: Theory and Practice of Auction Market Design

This class will focus on several topics in auction market design and related areas. It is an advanced course, intended as a sequel to the more basic market/mechanism/auction design courses offered at the Economics department and the GSB. Students are expected to be familiar with the material in those courses. We will briefly review some basics of auction theory, but the main goal of the class is to bring students closer to doing independent research and introduce them to recent contributions and currently active research areas. Specific topics may include: multi-item and combinatorial auctions; robust auction design; applied auction design with practical applications; matching and pricing on the Internet; radio spectrum auctions; securities markets; commodities; complex procurements. Grading based on presentation, assignment, and term paper.
Terms: Spr | Units: 2-5 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Milgrom, P. (SI)

ECON 285: Matching and Market Design

This is an introduction to market design, intended mainly for second year PhD students in economics (but also open to other graduates students from around the university and to undergrads who have taken undergrad market design). It will emphasize the combined use of economic theory, experiments and empirical analysis to analyze and engineer market rules and institutions. In this first quarter we will pay particular attention to matching markets, which are those in which price doesn't do all of the work, and which include some kind of application or selection process. In recent years market designers have participated in the design and implementation of a number of marketplaces, and the course will emphasize the relation between theory and practice, for example in the design of labor market clearinghouses for American doctors, and school choice programs in a growing number of American cities (including New York and Boston), and the allocation of organs for transplantation. Various forms more »
This is an introduction to market design, intended mainly for second year PhD students in economics (but also open to other graduates students from around the university and to undergrads who have taken undergrad market design). It will emphasize the combined use of economic theory, experiments and empirical analysis to analyze and engineer market rules and institutions. In this first quarter we will pay particular attention to matching markets, which are those in which price doesn't do all of the work, and which include some kind of application or selection process. In recent years market designers have participated in the design and implementation of a number of marketplaces, and the course will emphasize the relation between theory and practice, for example in the design of labor market clearinghouses for American doctors, and school choice programs in a growing number of American cities (including New York and Boston), and the allocation of organs for transplantation. Various forms of market failure will also be discussed.nAssignment: One final paper. The objective of the final paper is to study an existing market or an environment with a potential role for a market, describe the relevant market design questions, and evaluate how the current market design works and/or propose improvements on the current design.
Terms: Aut | Units: 2-5 | Grading: Letter or Credit/No Credit

ECON 286: Game Theory and Economic Applications

Aims to provide a solid basis in game-theoretic tools and concepts, both for theorists and for students focusing in other fields. Technical material will include solution concepts and refinements, potential games, supermodular games, repeated games, reputation, and bargaining models. The class will also address some foundational issues, such as epistemic and evolutionary modeling.Prerequisite: 203 or consent of instructor.
Terms: Aut | Units: 2-5 | Grading: Letter or Credit/No Credit
Instructors: Carroll, G. (PI)

ECON 288: Computational Economics

This course studies computational approaches for solving dynamic economic models. First, it provides background in numerical analysis (approximation, integration, optimization, error analysis), and describes local and global numerical methods (perturbation, Smolyak, endogenous grid, stochastic simulation, cluster grid methods). Then, it shows applications from recent economic literature representing challenges to computational methods (new Keynesian models with a zero lower bound, default risk models, Krusell-Smith models, international trade models, overlapping-generations models, nonstationary growth models, dynamic games). Finally, it surveys recent developments in software and hardware (Python, Julia, GPUs, parallel computing, supercomputers), as well as machine learning techniques. No prerequisites. Grading on the basis of problem sets and a final project.
Terms: not given this year, last offered Autumn 2017 | Units: 2-5 | Grading: Letter or Credit/No Credit

ECON 289: Advanced Topics in Game Theory and Information Economics

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.
Terms: Win | Units: 2-5 | Grading: Letter or Credit/No Credit
Instructors: Kojima, F. (PI)

ECON 290: 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, and computation.
Terms: Spr | Units: 3 | Grading: Satisfactory/No Credit
Instructors: Wilson, R. (PI)

ECON 291: Social and Economic Networks

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.
Terms: not given this year, last offered Spring 2017 | Units: 2-5 | Grading: Letter or Credit/No Credit

ECON 292: Quantitative Methods for Empirical Research

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.
Terms: Aut | Units: 2-5 | Grading: Letter or Credit/No Credit

ECON 293: 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.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Filter Results:
term offered
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints