31 - 40 of 46 results for: MGTECON

The course covers various topics relating to social insurance. 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 local public policy through the lens of social insurance, and includes topics such as spatial equilibrium, placed-based policies and housing policy.

This course is intended to be an introduction to dynamic political economy theory. We will cover research at the frontier of this field and some useful tools. Tools will be primarily dynamic game theory - including Markov models and models of reputation. Topics covered will include dynamic legislative bargaining, dynamic coalition formation, endogenous institutions, endogenous policy formation, and policy experimentation.

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.nnThe 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.nnWith 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.nnCoursework 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 will provide an overview of recent advances in, and applications of, dynamic oligopoly models in I.O. We will start by introducing a simple framework for dynamic oligopoly in the context of a dynamic investment model. We will move on to other applications and extensions of the framework, including dynamic entry models and dynamic mergers, with a discussion of antitrust issues. We will cover an empirical model of dynamic network adoption and participation. We will learn alternative econometric approaches to the identification and estimation of dynamic oligopoly models, including a discussion of serially correlated unobserved shocks. Finally, we will discuss methods for computing counterfactuals and welfare, and then speculate about some unresolved issues and the potential for future work in this area.

This course meets weekly on Tuesdays at Noon. 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 once 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 seminar-style course studies a selection of micro-economic models in dynamic settings, and explores the use of continuous-time methods to solve them. Topics to be covered include experimentation games, social learning, principal-agent problems, career concerns/market-agent models, security design and strategic trading. For every topic discussed, the class introduces gradually the set of relevant mathematical tools: dynamic programming and Hamilton-Jacobi-Bellman equations, Pontryagin's maximum principle, Euler-Lagrange equations, Brownian and Poisson processes, Bayesian inference and linear filtering, change of measure, martingale representation, Malliavin derivatives, stochastic maximum principle, expansions of filtrations. nThe course emphasizes high-level intuition rather than mathematical rigor. It is targeted at those who seek to become familiar with the literature on continuous-time dynamics and want to understand the functioning of these models, either by general interest or to apply these techniques. n

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. We consider the estimation of average treatment effects as well as personalized policies. Applications to the evaluation of large-scale experiments, including online A/B tests and experiments on networks, will receive special attention.