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.

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. Grades will be based on homework problem sets (20%), class participation (10%), and a comprehensive final examination (100%).

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.

This is the second course in the microeconomics sequence. It consists of two parts. In the first part, we learn and use game theory to think carefully and systematically about strategic interactions. The second part considers problems of information: Asymmetric information, communication, screening, contracts, (simple) mechanisms. Throughout the course, we discuss applications in fields like corporate strategy, international relations, market regulation and public policy.

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 a two-part sequence designed to familiarize students with the basic concepts of statistics and econometrics. Building on the material from the first course on Statistics, it deepens students’ understanding of econometric theory and develops practical skills in data analysis and management as well as causal inference methods. The course focuses on regression models and their extensions, including hypothesis testing and identification issues. Additional topics include the basics of time series and panel data analysis, as well as an introduction to difference-in-differences, regression discontinuity designs, and instrumental variable estimation used in program evaluation. A central focus is on applying theoretical concepts to real-world data using statistical software such as Stata.

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 course introduces students to programming for empirical economic analysis using Python (or R). Students learn how to import, clean, transform, visualize, and analyze economic data while developing good programming and reproducibility practices.
The course covers data manipulation, visualization, simulations, time-series and spatial data, as well as basics of working with web data and image analysis. Throughout the course, students apply these techniques to practical economic problems using real-world datasets.
By the end of the course, students will be able to write reproducible code, manage and visualize data, and perform basic empirical analyses relevant to economics and related social sciences.

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).

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.

This is the first part of the Public Finance sequence. The second part will be taught by Teresa Freitas-Monteiro in Spring 2027.

The title of this part could be ‘Macroeconomic aspects of public finance’. As such, this course can be divided into two parts:

Part 1. In order to discuss issues related to taxation in macroeconomic heterogeneous-agent models, we will first need to set up these models. This will be the first part of the course. Here, we will discuss issues related to heterogeneity and inequality. We will start by characterizing the solution to a heterogeneous agent model with complete markets and conclude that the dynamics implied by the model are not consistent with real-world data. We will then analyze models in which markets are (exogeneously) incomplete - either because not all assets are traded (there is only one risk-free asset) or there are borrowing constraints. First, we will characterize the solution to individual agents' problems when the interest rate is fixed (partial equilibrium) with, first, deterministic and, second, stochastic, income fluctuations. Next, we will study general equilibrium versions of these models without and with aggregate risk.

Part 2. In the second part of the course, we will move on to discussing papers that are studying taxation in heterogeneous agent models.

The course will discuss various experimental approaches, such as lab experiments, lab-in-field experiments, randomized control trials, and survey experiments. The focus will be on (i) experiments that test ideas from behavioral economics (social preferences, social norms, identity, time discounting and limited self-control, limited attention, etc.) and (ii) experiments that are primarily motivated by important economic and social issues (poverty, discrimination, inter-group conflicts). More broadly, the course aims to show the value of primary data collection in terms dealing with identification issues, testing competing theoretical predictions and more precise measurement.

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.

The aim of this course is to offer students systematic and rigorous tools in order to evaluate the impacts of a wide spectra of public policies (in the field of labour, education, social issues and firm subsidies). Students will learn how to assess impacts of polices through controlled field experiments, ex-post evaluation methods that identify causal impacts of policies based on observational data, and incorporate impact assessments directly into policy making. The course aims to equip students with the tools to critically assess the design and impact of public policies in different sectors, using data-driven approaches to inform evidence-based policy decisions. The course is divided into two parts. In the first part, students are provided with main identification and empirical strategies used nowadays in public policy evaluation. The second part of the course is targeted on the real public policy issues and on the evaluation of these policies. Under the supervision and help of the lecturer, students divided into small research teams will further evaluate the impact of already existing policies based on the data or suggest a new public policy and predict its impact.

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.

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.

The objective of this course is to introduce students to the core topics in Public Economics at the graduate level. Public Economics studies the role of the government in the economy and the implications of its policies for individuals. In this course, we will analyze how market failures can create a potential role for government intervention and study the issues that can arise when governments operate under imperfect information. The course evaluates both the efficiency and equity implications of public policies, studying how interventions affect individual incentives, behavioral responses, and welfare. Throughout the semester, we will cover topics such as tax policy, inequality, social insurance, and public goods. The course will combine theoretical models with empirical work and will cover both classical and recent studies. Students should have a work-level knowledge of Microeconomics Theory and Econometrics/Policy Evaluation.

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.

We will study papers that address topics of global interest. The topics, subject to change, include income and wealth inequality, firm market power, and labor market institutions.

The master thesis seminar guides second-year students through the process of writing their master thesis. In addition to clarifying the formal requirements of a master thesis, the seminar sets a series of milestones the students will need to achieve on the way to a successful defense.