31 - 40 of 41 results for: MGTECON

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 short final research project. There will also be room for short student presentations (related to homework assignments, economic papers, or definitions and results related to specific math concepts).

The primary purpose of the course is to read and discuss current working papers in Industrial Organization and related fields (e.g., Econometrics, Marketing, and Labor). Students are required to present papers a couple of times per quarter and both students and faculty may also present their own working papers.

Each week, a different economics faculty member will discuss his or her important and /or current research. The course is an important introduction to PhD level research topics and techniques. Attendance is mandatory.

This is an introductory course in Industrial Organization. The goal is to provide broad general training in the field, introducing you to the central questions around imperfect competition, market structure, innovation and regulation, as well as the models and empirical methods commonly used to tackle these questions.

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: Prior coursework in empirical methods for causal inference in observational studies, including instrumental variables, fixed effects modeling, regression discontinuity designs, etc. Students should be comfortable reading and engaging with empirical research in economics or related fields.

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 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.

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.

Doctoral Practicum in Teaching