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141 - 150 of 164 results for: ECON

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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
Instructors: Carroll, G. (PI)

ECON 288: Computational Economics

Overview of numerical analysis. Computational approaches to solving economic problems, including dynamic programming, projection and perturbation. General equilibrium models, new Keynesian models, Krusell-Smith model, default risk models, international trade models, and dynamic games. Numerical methods for large-scale applications (Smolyak, endogenous-grid, stochastic simulation, epsilon-distinguishable set algorithms). Parallel computation, GPUs and supercomputers. Prerequisite: equivalent of first-year graduate core economics sequence.
Last offered: Autumn 2015

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
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
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: Spr | Units: 2-5
Instructors: Jackson, M. (PI)

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
Instructors: Imbens, G. (PI)

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. Recent advances in supervised and unsupervised machine learning provide systematic approaches to model selection and prediction, methods that are particularly well suited to datasets with many observations and/or many covariates. 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. Applications to the evaluation of large-scale experiments, including online A/B tests and experiments on networks, will receive special attention. We will also consider topic modeling, Bayesian methods, and a brief overview of textual analysis.
Terms: Spr | Units: 3
Instructors: Athey, S. (PI)

ECON 299: Practical Training

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. 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.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
Instructors: Abramitzky, R. (PI) ; Admati, A. (PI) ; Amador, M. (PI) ; Amemiya, T. (PI) ; Aoki, M. (PI) ; Arora, A. (PI) ; Arrow, K. (PI) ; Athey, S. (PI) ; Attanasio, O. (PI) ; Auclert, A. (PI) ; Bagwell, K. (PI) ; Baron, D. (PI) ; Bekaert, G. (PI) ; Bernheim, B. (PI) ; Bettinger, E. (PI) ; Bhattacharya, J. (PI) ; Blimpo, M. (PI) ; Bloom, N. (PI) ; Boskin, M. (PI) ; Brady, D. (PI) ; Bresnahan, T. (PI) ; Bulow, J. (PI) ; Canellos, C. (PI) ; Carroll, G. (PI) ; Chandrasekhar, A. (PI) ; Chaudhary, L. (PI) ; Chetty, R. (PI) ; Clerici-Arias, M. (PI) ; Cogan, J. (PI) ; Cojoc, D. (PI) ; David, P. (PI) ; DeGiorgi, G. (PI) ; Dickstein, M. (PI) ; Donaldson, D. (PI) ; Duffie, D. (PI) ; Duggan, M. (PI) ; Dupas, P. (PI) ; Einav, L. (PI) ; Fafchamps, M. (PI) ; Falcon, W. (PI) ; Fitzgerald, D. (PI) ; Fitzpatrick, M. (PI) ; Fong, K. (PI) ; Fuchs, V. (PI) ; Garber, A. (PI) ; Gentzkow, M. (PI) ; Gould, A. (PI) ; Goulder, L. (PI) ; Greif, A. (PI) ; Haak, D. (PI) ; Haber, S. (PI) ; Hall, R. (PI) ; Hammond, P. (PI) ; Hansen, P. (PI) ; Hanson, W. (PI) ; Hanushek, E. (PI) ; Harding, M. (PI) ; Harris, D. (PI) ; Hartmann, W. (PI) ; Henry, P. (PI) ; Hickman, B. (PI) ; Hong, H. (PI) ; Hope, N. (PI) ; Horvath, M. (PI) ; Hoxby, C. (PI) ; Imbens, G. (PI) ; Jackson, M. (PI) ; Jagolinzer, A. (PI) ; Jaimovich, N. (PI) ; Jarosch, G. (PI) ; Jayachandran, S. (PI) ; Jones, C. (PI) ; Jost, J. (PI) ; Judd, K. (PI) ; Kastl, J. (PI) ; Kehoe, P. (PI) ; Kessler, D. (PI) ; Klenow, P. (PI) ; Kochar, A. (PI) ; Kojima, F. (PI) ; Kolstad, C. (PI) ; Krueger, A. (PI) ; Kuran, T. (PI) ; Kurlat, P. (PI) ; Kurz, M. (PI) ; Lambert, N. (PI) ; Larsen, B. (PI) ; Lau, L. (PI) ; Lazear, E. (PI) ; Levin, J. (PI) ; MaCurdy, T. (PI) ; Mahajan, A. (PI) ; Malmendier, U. (PI) ; Manova, K. (PI) ; McClellan, M. (PI) ; McKeon, S. (PI) ; McKinnon, R. (PI) ; Meier, G. (PI) ; Milgrom, P. (PI) ; Miller, G. (PI) ; Morten, M. (PI) ; Moser, P. (PI) ; Naylor, R. (PI) ; Nechyba, T. (PI) ; Niederle, M. (PI) ; Noll, R. (PI) ; Owen, B. (PI) ; Oyer, P. (PI) ; Pencavel, J. (PI) ; Persson, P. (PI) ; Piazzesi, M. (PI) ; Pistaferri, L. (PI) ; Polinsky, A. (PI) ; Qian, Y. (PI) ; Rangel, A. (PI) ; Reiss, P. (PI) ; Richards, J. (PI) ; Roberts, J. (PI) ; Romano, J. (PI) ; Romer, P. (PI) ; Rosenberg, N. (PI) ; Rossi-Hansberg, E. (PI) ; Rosston, G. (PI) ; Roth, A. (PI) ; Rothwell, G. (PI) ; Royalty, A. (PI) ; Rozelle, S. (PI) ; Sargent, T. (PI) ; Schaffner, J. (PI) ; Scheuer, F. (PI) ; Schneider, M. (PI) ; Segal, I. (PI) ; Sharpe, W. (PI) ; Shotts, K. (PI) ; Shoven, J. (PI) ; Singleton, K. (PI) ; Skrzypacz, A. (PI) ; Sorkin, I. (PI) ; Sprenger, C. (PI) ; Staiger, R. (PI) ; Stanton, F. (PI) ; Sweeney, J. (PI) ; Taylor, J. (PI) ; Tendall, M. (PI) ; Tertilt, M. (PI) ; Topper, M. (PI) ; Vytlacil, E. (PI) ; Wacziarg, R. (PI) ; Weingast, B. (PI) ; Wilson, R. (PI) ; Wolak, F. (PI) ; Wolitzky, A. (PI) ; Wright, G. (PI) ; Wright, M. (PI) ; Yotopoulos, P. (PI)

ECON 300: Third-Year Seminar

Restricted to Economics Ph.D. students. Students present current research. May be repeated for credit.
Terms: Aut, Spr | Units: 1-10 | Repeatable for credit
Instructors: Bresnahan, T. (PI) ; Persson, P. (PI)

ECON 310: Macroeconomic Workshop

Terms: Aut, Win, Spr | Units: 1-10 | Repeatable for credit
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