Guillaume Saint‑Jacques

Guillaume Saint-Jacques

Ph.D. Candidate
Information Technologies / Economics
MIT Sloan School of Management
gsaintja@mit.edu

Guillaume is a Ph.D. candidate at MIT Sloan. His advisor is Erik Brynjolfsson, who also serves as the director of the MIT Initiative on the Digital Economy. Guillaume's research interests involve the Economics of Information Technologies and Digitization. In particular, he focuses on understanding the link between information technologies and the distribution of productivity and income, both across individuals and across businesses.

Prior to joining MIT, Guillaume completed a Master's degree in Economics at ENS Paris (promotion 2006) and Paris School of Economics. Under the supervision of Thomas Piketty, he wrote his Master's thesis on the effect of income tax schedules that involve income splitting on the labor supply of women, and he developed the first on-line tax reform simulator. He received his business education in the Entrepreneurship program at HEC Paris. For a year, he also taught as a French lecturer at the University of California, Los Angeles.

Graduate Level Coursework

Only coursework relevant to my research is shown. Please click on course titles for a short description.

Economics

Micro I (P. Pathak, MIT)
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.
Micro II (G. Ellison, MIT)
Introduction to game theory. Topics include normal form and extensive form games, and games with incomplete information. Enrollment limited.
Micro III (J. Noor, BU)
Models of individual decision-making under certainty and uncertainty. Additional topics in game theory. Enrollment limited.
Micro IV (B. Holmström, MIT)
Decision-making under uncertainty, information economics, incentive and contract theory. Enrollment limited.
Industrial Organization I (G. Ellison, MIT)
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.
Industrial Organization II (N. Rose, MIT)
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.
Topics in Inequality* (D. Acemoglu, D. Autor, I. Werning, MIT)
Addresses empirical and theoretical issues of inequality from various perspectives, such as macroeconomic, labor, public finance, and political economy.

Econometrics and Machine Learning

Statistical Methods in Economics (A.Mikusheva, MIT - V.Chernozukhov, MIT)
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. Enrollment limited.
Econometrics (J. Hausman, MIT)
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.
Time Series Analysis (A.Mikusheva, MIT)
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.
Topics in Applied Econometrics (J.Angrist, MIT)
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 organizational behavior. 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.
Introduction to Databases* (J. Widom, Stanford - Online Class)
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

Contact

Guillaume Saint-Jacques
MIT Sloan School of Management, E62-359
100 Main Street
Cambridge, MA 02142
Email: gsaintja@ mit.edu

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