Graduate Level Coursework
Only coursework relevant to my research is shown. Please click on course titles for a short description.
Covers consumer and producer theory, markets and competition, and general equilibrium.
Covers the tools of identification in price and general equilibrium theory,
the fundamental welfare theorems, aggregation, and applications.
Enrollment limited; preference to PhD students.
Introduction to game theory. Topics include normal form and extensive form games,
and games with incomplete information. Enrollment limited.
Models of individual decision-making under certainty and uncertainty. Additional topics in game theory. Enrollment limited.
Decision-making under uncertainty, information economics, incentive and contract theory. Enrollment limited.
Covers theoretical and empirical work dealing with the structure, behavior,
and performance of firms and markets and core issues in antitrust. Topics include:
the organization of the firm, monopoly,
price discrimination, oligopoly, and auctions. Theoretical and empirical work are integrated in each area.
Topics covered include horizontal mergers and demand estimation,
vertical integration and vertical restraints, natural monopoly and its regulation, public enterprise,
political economy of regulation, network access pricing, deregulation of telecommunications,
electric power, cable television, transportation sectors, and risk and environmental regulation.
Economics of Information and Technology in Markets and Organizations
Builds upon relevant economic theories and methodologies to analyze the changes in organizations
and markets enabled by IT, especially the internet. Typical perspectives examined include industrial
organization and competitive behavior, price theory, information economics, intangible asset valuation,
consumer behavior, search and choice, auctions and mechanism design, transactions cost economics and
incomplete contracts theory, and design of empirical studies. Extensive reading and discussion of
research literature aimed at exploring the application of these theories to business issues and
challenges raised by the internet and related technologies. Primarily for doctoral students.
Addresses empirical and theoretical issues of inequality from various perspectives, such as macroeconomic, labor, public finance, and political economy.
Econometrics and Machine Learning
Introduction to probability and statistics as background for advanced econometrics
and introduction to the linear regression model. Covers elements of probability theory;
sampling theory; asymptotic approximations; decision-theory approach to statistical
estimation focusing on regression, hypothesis testing; and maximum-likelihood methods.
Includes simple and multiple regression, estimation and hypothesis testing.
Illustrations from economics and application of these concepts to economic problems.
Regression analysis, focusing on departures from the standard Gauss-Markov assumptions,
and simultaneous equations. Regression topics include heteroskedasticity, serial correlation,
and errors in variables, generalized least squares, nonlinear regression, and limited dependent
variable models. Covers identification and estimation of linear
and nonlinear simultaneous equations models. Economic applications are discussed. Enrollment limited.
Studies theory and application of time series methods in econometrics, including spectral analysis,
estimation with stationary and non-stationary processes, VARs,
factor models, unit roots, cointegration, estimation of DSGE models, and Bayesian methods. Enrollment limited.
Covers core econometric ideas and widely used empirical modeling strategies.
Topics vary from year to year, but course typically begins with instrumental variables, concepts,
and methods; then moves on to discussion of differences-in-differences and regression discontinuity methods.
Concludes with discussion of standard errors, focusing on issues such as clustering and serial correlation.
Advanced introduction to the theory and application of statistics, data-mining, and machine learning,
concentrating on techniques used in management science, finance, consulting, engineering systems,
and bioinformatics. First half builds the statistical foundation for the second half, with topics
selected from sampling, including the bootstrap, theory of estimation, testing, nonparametric statistics,
analysis of variance, categorical data analysis, regression analysis, MCMC, EM, Gibbs sampling, and Bayesian methods.
Second half focuses on data mining, supervised learning, and multivariate analysis.
Topics selected from logistic regression; principal components and dimension reduction; discrimination and
classification analysis, including trees (CART), partial least squares, nearest neighbor and regularized methods,
support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression.
Uses statistics software packages, such as R and MATLAB for data analysis and data mining. Term project required.
Other perspectives on IT: Social Science, CS
This course will examine the foundations of and recent advances in Network Theory, Network Science and Applied Network Analysis from sociological, economic and statistical perspectives. The course is aimed at doctoral students conducting original research in applied network theory and analysis in a diverse set of fields including sociology, economics, statistics, computer science/machine learning, management, computational biology and physics. The course will follow a research seminar format, with deep critical examinations of original research papers from these disciplines, designed to teach networks research through an evaluation of networks research. Topics covered include: network structure, foundations of sociological network theory, weak ties and structural holes, embeddedness, homophily and assortative mixing, information diffusion in networks, small world phenomena, influence maximization in networks, statistical inference in networks, causal inference in networks, networks and coordination, network dynamics, networked experiments, estimating peer effects, networked interventions and more.
Covers classic and contemporary theories and research related to individuals, groups, and organizations.
Designed primarily for doctoral students in the Sloan School of Management who wish to familiarize themselves
with research by psychologists, sociologists, and management scholars in the area commonly known as micro
Topics may include motivation, decision making, negotiation, power, influence, group dynamics, and leadership.
Examines the assumptions, concepts, theories, and methodologies that inform research into the social aspects of
information technology. Extensive reading and discussion of research literature aimed at exploring micro, group,
and macro level social phenomena surrounding the development, implementation, use and implications of information
technology in organizations. Primarily for doctoral students.
This course covers database design and the use of database management systems for applications.
It includes extensive coverage of the relational model, relational algebra, and SQL. It also covers XML
data including DTDs and XML Schema for validation, and the query and transformation languages XPath, XQuery,
and XSLT. The course includes database design in UML, and relational design principles based on dependencies
and normal forms. Many additional key database topics from the design and application-building perspective are
also covered: indexes, views, transactions, authorization, integrity constraints, triggers, on-line analytical
processing (OLAP), JSON, and emerging "NoSQL" systems.
Classes at Paris School of Economics [+]
*: classes taken as a listener