The objective of this course is for students to develop a comprehensive understanding of modern macroeconomic theory and its empirical applications through the lens of real business cycle models and advanced econometric methods. Students will master the theoretical foundations of consumption behavior, investment decisions, and labor market dynamics, culminating in the construction and analysis of dynamic stochastic general equilibrium models. The course bridges microeconomic foundations with macroeconomic phenomena, examining how technology shocks propagate through the economy and drive business cycle fluctuations. At the end of the course, students should be able to critically evaluate macroeconomic policies through rigorous theoretical modeling, assess the role of frictions in labor markets as sources of unemployment, and apply sophisticated time series techniques to analyze real-world economic data. The class integrates theory with hands-on empirical work, utilizing publicly available macroeconomic datasets and implementing econometric models in R through regular programming assignments. Students will gain expertise in structural VAR identification methods, narrative approaches to causal inference, and extensions of growth theory incorporating human capital and innovation.

This is the first course in a sequence of 2 courses, along with Game Theory and Information Economics, that endows students with the basic insights of microeconomic theory. The goal of the course is to introduce students into the way economists see human interactions in markets, with markets broadly defined as places where economic activity takes place. The provided body of knowledge will allow student to understand how demand and supply interact in markets and welfare properties of the outcomes of the interactions.

This course is the first part of the statistics sequence in the master’s program and introduces students to statistics and data science from a practical, application-oriented perspective. We cover probability theory, regression methods, causal inference, and machine learning, with emphasis on tools used in modern applied economics. While econometric theory is introduced when needed, the course is intentionally hands-on: students work frequently with real data, complete regular programming assignments, and develop an empirical research project over the semester. Assessment consists of weekly assignments, a final empirical project (idea submission, idea presentation, project presentation, and final paper), and a short final exam.

We will study a few papers that address current issues in government policy related to inflation and debt.  Afterwards, we will continue with additional papers if time permits.  Half of the classes will consist of my covering the contents of the papers, including a discussion of your questions.  The other half will consist of your presentations of papers and possibly other materials.

This is the second course in the microeconomics sequence, and it builds heavily on the first course on microeconomics I. The course consists of two parts. In the first part, the accent will be on a bit more rigorous and more general treatment of monopoly, Game theory, and imperfect competition. The second part will introduce the study of incomplete information and foundational concepts in information economics. Each concept will be followed by a discussion of its economic applications.

This is the second course in a two-part sequence designed to familiarize students with the basic concepts of statistics and econometrics. Building on the material from the first course, Statistics: Foundations of Data Science, it deepens students’ understanding of econometric theory and develops practical skills in data analysis and management. The course focuses on regression models and their extensions, including hypothesis testing, identification issues, and instrumental variable estimation. Additional topics include the basics of time series and panel data analysis, as well as an introduction to ordered response models and maximum likelihood methods such as probit and logit estimation. A central focus is on applying theoretical concepts to real-world data using statistical software such as R.

The course focuses on professional writing in Economics in English in a variety of genres, and considers how AI can and cannot be used ethically and appropriately to produce texts. Students practice analytical writing skills in formal, post-graduate level English. There is an emphasis on academic integrity, and the types of grammatical structures and language used in a variety of professional texts in the field. The course includes lectures, peer input on the main tasks throughout development of the work, and individual consultations with the instructor. Extensive written feedback is given with a view to supporting future work. The main tasks are a position paper and presentation of the paper. The paper should be relevant to the student’s planned thesis. The skills practiced on this course support student writing and speaking throughout their studies and beyond into real-world contexts. The RW1 course includes a focus on development of the required Topic Request Submission paper. Students are required to agree with a faculty member who will chair their thesis and to choose an thesis topic by April 3.

This is a second-year graduate-level course. The course is based on selected and (mostly) recent empirical research papers focusing on particular aspects of the economic history of the United States, paying particular attention to the topics of internal and international migration, cities, innovation, and culture. Beyond providing students with an in-depth understanding of the research frontier in US economic history, the course will focus on developing skills in developing, communicating, presenting, and evaluating research ideas and causal research designs in applied economics more broadly.

The course will provide fundamental understanding of stylized labor supply and demand in their static and advanced versions, and associated models of wage determination. The course will combine theoretical concepts, empirical evidence and empirical methods including use of econometrics and individual level data. Policy and mechanism designs debates involving students will be encouraged.
The course has three major goals (i) to guide students through current theoretical and empirical understanding of major labor market issues, (ii) to promote student’s own empirical research on topic selected, (iii) to make students familiar with stylized research resources, field standards and approaches. Throughout the topics, empirical methodological approaches will be clarified (data and econometric / identification techniques).

The goal of the course is to introduce tools necessary to understand and implement empirical studies (evaluations of causal effects) with cross-sectional and panel data. Heterogeneous treatment effects and dynamic panel data models fall outside of the scope of the course, as do machine learning techniques and AI. Examples from applied work will be used to illustrate the discussed methods. Note that the course covers much of the work of the Nobel prize laureates for 2000 and 2021. The main reference textbook for the course is Econometric Analysis of Cross Section and Panel Data, Jeffrey M. Wooldridge, MIT Press 2002. I provide suggestions for reading and additional references throughout the lecture notes (available on my homepage).

This graduate course introduces the empirical and structural methods used to study markets, firm behavior, and competition. It focuses on econometric techniques for modeling and estimating demand, production, and strategic interactions among firms. The course covers discrete choice models, production function estimation, dynamic games, and network effects, and emphasizes methodological aspects regarding identification, simulation methods, and structural estimation. The course emphasizes both methodological understanding and computational implementation using modern econometric tools, preparing students to conduct independent research in industrial organization and applied microeconometrics.

This course is the second step in student’s ongoing practice of their professional communications skills in the broad field of economics. It includes written tasks, a negotiation, and presentations, and continues the collaborative features of Research Writing 1. Lectures, discussions, teamwork, and individual consultations with the instructor are aimed to continue to build student’s skills and confidence, and to provide useful take-aways for real-world endeavors. The skills practiced on this course are designed to support student writing and speaking throughout their studies and beyond into real-world contexts. The RW2 course includes a focus on students’ early development of their required Master’s thesis. Development of the thesis will be supported via in-class work and individual consultation with the instructor.

This graduate-level course introduces machine learning techniques and applications tailored for the social sciences. It aims to equip students with essential tools to apply machine learning in different areas, including causal inference and time-series analysis. The course combines practical Python applications with foundational statistical methods. Topics include generalized linear models, decision trees, and neural networks, providing a solid foundation in core machine learning approaches. By the end of the course, students will have a comprehensive understanding of key machine learning paradigms.

In this course, we focus on three topical issues that affect modern labor markets: migration, technological development, and inequality. In the first part, we study the determinants of migration, impacts on host countries' labor markets, attitudes towards migrants, and refugee migration. In the second part, we review the literature on the effects of automation and artificial intelligence (AI) on the labor market. We discuss the central questions of whether AI is "different'' from other technological shocks, and how it will affect workers across the skill and wage distribution. The third part focuses on inequality, intergenerational mobility, and discrimination. Using the example of job recommendation systems, we will also critically discuss the risks of adopting AI in public policy.

Why are some countries rich and others poor? This course explores how societies develop—and why poverty persists—through a comparative approach grounded in economic history, culture, and political economy. We study whether contemporary development differences have historical origins and analyze the channels through which history shapes development, focusing on domestic institutions, culture, and geography. Examples include the legacies of the slave trade, colonialism, and religion. The course builds on teaching material from Harvard University and covers both foundational contributions—such as work by Nobel laureates Acemoglu, Johnson, and Robinson—and the research frontier of recent years. Although the focus is on economic methods, the questions intersect with history, psychology, political science, anthropology, and geography. Students learn rigorous empirical methods, including identification strategies such as instrumental variables and regression discontinuity designs, as well as survey data collection, randomized controlled trials, and GIS tools. Each student develops an original research project, presents it throughout the course, and submits a final version

The course will bring students to the research frontier in applied economics with a special emphasis on economic history and long-run development. The course consists of weekly lectures and seminars in which we discuss topics such as pre-industrial development, industrialization, the formation of norms for long-run economic outcomes, war economics, the economics of crises, the economics of totalitarian regimes, regional development after World War II and more recent figures of economic growth, transition, and monetary integration. The lectures will provide stylized facts and underlying theoretical concepts, while we will critically discuss recent empirical research papers on the respective topics during the seminars. The course further consists of Stata and R assignments in which students will challenge published papers with newly established empirical methodologies. Finally, students have to present and write their own research proposal in the field of quantitative economic history.

The main topics of the class are econometric approaches to the problem of sample selection and (individual-level) heterogeneity. While the methods apply more generally, the class will focus on methods to address the selection problem from the program evaluation literature and place particular emphasis on heterogeneity in randomized control trials in the second part of the course

This course focuses on the theoretical study of market power, covering key concepts and models in static and dynamic oligopoly theory, along with their applications. It examines how firms behave in industries where a few competitors interact strategically, meaning they must consider each other's actions. These strategic interactions have both positive (e.g., pricing, market structure, innovation intensity) and normative implications (e.g., competition policy). While the emphasis is on positive analysis, the course also frequently addresses the normative aspects.