Curriculum

The course surveys the ideas and concepts relating to consumers, producers and their interaction, it will provide technical means to solve practical problems and will also follow the development of these concepts over the years.

1. The first part of the course will introduce the tools and techniques for solving dynamic economic models, in particular deterministic and stochastic discrete-time dynamic programming. We will apply the tools to study the neoclassical growth model, consumption and saving choices, and labor search. Basic knowledge of Matlab will be required to solve some of the problem sets.

2. Recursive methods constitute a powerful approach in dynamic economics, as they focus on the tradeoff between current-period utility and the continuation value of utility in future periods.
This part of the course will build on several key ideas: the competitive equilibrium model of a dynamic stochastic economy, complete and incomplete markets. This framework underlies asset pricing theory, growth theory, real business cycle theory, and normative public finance. To incorporate fiat money into this model, a modification is required. The shopping time model will then be used to illustrate ten core doctrines of monetary economics.

This graduate-level course introduces the mathematical foundations of probability and statistics essential for advanced studies in econometrics and microeconomics. It covers probability theory, random variables, univariate and multivariate distributions, and sampling principles. The course covers key estimation techniques—method of moments and maximum likelihood—as well as hypothesis testing, interval estimation, and asymptotic theory, including Laws of Large Numbers and Central Limit Theorems. The course concludes with an introduction to linear projections and regression analysis, providing a rigorous basis for further econometric applications. Emphasis is placed on both theoretical understanding and problem solving.

This course is the second in a three-semester microeconomics sequence for first-year Ph.D. students, focusing on non-cooperative game theory and its applications. Game theory is a widely used mathematical tool for modeling and analyzing interactive decision-making situations. The course aims to introduce students to normal-form (static) and extensive-form (dynamic) games with complete and incomplete information. It will also cover some basic economic applications of game theory, preparing students for more advanced studies.

1. This course is both about learning technical tools and about applying them. We will set up a pretty general CE model with infinite horizon, heterogenous firms and consumers. We will prove the First Welfare Theorem and discuss the Second Welfare Theorem for this environment. Then we will show how to simplify the model to the deterministic one sector growth model (aggregation). In the next part of the course, we will extend the model to account for the 2 most important features of current economies: long run growth (briefly as already done in Macro 1) and business cycle fluctuations. Then, we will be interested in the government's role in the economy and the optimal fiscal policies. We will set up the classic Ramsey linear taxation problem and derive the Chamley-Judd result, which states that optimal taxes on capital are zero in the long run.

2. The second half of the course will focus on general equilibrium models of endogenous economic growth in continuous time. It will begin with the basics of dynamic optimization in continuous time and an outline of the stylized facts of economic growth. After applying dynamic optimization in continuous time to the Ramsey neoclassical growth model, including government and taxes, this part of the course will cover the first generation of endogenous growth models and models with endogenous technological change. Additionally, the simple overlapping generations model and the finite-lifetime model will be discussed.

The course presents technical aspects of modern econometric estimation and inference, applied in both cross-sectional and time-series settings. After reviewing important econometric notions and asymptotic inference tools, we concentrate on parametric regression models, including linear and nonlinear ones. Then we turn to methods applied to non-regression settings, including maximum likelihood and method of moments estimation. Finally, we will study methods of bootstrap inference.

The course focuses on PhD/professional level writing in Economics. We consider ways that writing at this level differs from other types of writing. Students will practice their analytical writing skills in formal, post-graduate level English. We consider ways AI can and cannot be ethically and appropriately applied in written work.  There is an emphasis on accurate and effective citation and referencing, and the types of language used in professional texts in the field. The course includes lectures, peer review during the writing process, and individual consultations with the instructor. Extensive written feedback on the work is given with a view to supporting future writing. The final task is a written analytical comparison of two related articles in the field of Economics. Students choose articles/topics that reflect their personal interests in the field, and the paper may form a basis for future papers. The skills practiced on this course are designed to support student writing throughout their first two years of study and beyond.

This is the third and final course in the core micro sequence. It focuses on information economics. We start by considering what information is, and how rational agents learn from information. Then we consider problems of hidden information and adverse selection, and how they can be solved: Through communication (signaling, cheap talk, verifiable information and communication with full commitment) and screening. Problems of hidden action can lead to moral hazard, and we consider principal agent problems as well as the theory of contracts. The second half of the course focuses on mechanism design in general and by considering specific mechanisms (auctions and matching algorithms).

We will study a few papers that represent the current issues in applied macroeconomics such as how to explain the recent evolution of macroeconomic indicators, how to manage inflation, and how the US monetary policy affects the rest of the world. The emphasis in on how macro-economists address applied and policy-relevant issues rather than introducing new macroeconomic theory.

This course is the third part of the Econometrics sequence. The first two weeks, the course will cover non-parametric mean regression. From the third week on, we will build on the techniques from Econometrics I and start with binary choice models for cross sectional data. Next, we will discuss methods applied to panel data. Last, we will delve into causal inference, based on the methods previously learned and new methods introduced.

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.

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 prerequisites are principles of microeconomic theory, statistics and econometrics from the 1st year.

We will study several papers that represent the current state of macroeconomic research. The topics include financial flows across countries, the interaction between fiscal policy and foreign debt management, and inflation driven by fiscal policy. We will also choose additional papers to study in class.

This course covers important aspects of modeling the dynamics of time series data, from both macroeconomics and finance. We will first discuss principles of time series modeling and model selection procedures. Then, we will study models for conditional mean dynamics such as linear and nonlinear autoregressions, primarily related to macroeconomic data, as well as models with unobserved processes. We will also study methods of dealing with structural instability and the notion and applications of Brownian motion. Then, we will turn to modeling conditional variance and other measures of financial return volatility. Finally, we will cover modeling conditional quantiles, probabilities and densities, related to both macro and financial data.

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.

AW II supports ongoing development of professional English and skills required to produce MA/PhD academic papers and publishable texts through explicit study of in-field language and genre. Students process their writing via continuing revision of draft work from the idea stages to the final version, in response to peer and instructor feedback. Instructors provide individual consultations and extended written feedback, aimed to support each student in developing his/her individual writing skills. A peer feedback process is applied to all written submissions, and a peer review of the final paper draft comprises a significant portion of the course grade. Building upon the work in Academic Writing 1, each student will research, plan, and write a practice Research Proposal on a topic of his or her choice.  The paper should both analyze the work of others and present the students’ own distinct position on the topic. The paper will be marked on its exposition; the economic ideas expressed in it are not evaluated on ASC courses. This paper can form a basis or thought exercise for a more developed research proposal in spring 2027.

The course aims at getting the second year Ph.D. students familiar with the basics and subtleties of how the academic economic science works and how academic economists do research, publish academic papers, make academic presentations and find their jobs. We will also review resources available on the Internet and aspects of academic integrity.

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.

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.

1. The increasing complexity in the analysis of theoretical and applied dynamic macroeconomic models—primarily due to the lack of available analytic solutions for most of them, and when they do exist, they are often trivial simplifications of the original problem—necessitates the use of efficient numerical methods in macroeconomics. A half of this part of the course is devoted to elementary concepts of numerical analysis and basic numerical methods, while the second half focuses on numerical methods for solving dynamic macroeconomic models. Students will be expected to write their own simple programs and run application programs and toolboxes in MATLAB.
2. In this course, we will discuss issues related to heterogeneity and inequality. We will start by characterizing the solution to a heterogeneous agents 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.

This module constitutes the second half of the course “Modeling in Macro and Finance.” It focuses on empirical finance, with applications to corporate finance, empirical banking, and the finance-and-growth nexus. The aim of this portion of the course is to equip students with a methodological toolkit and practical understanding of cross-sectional and panel-data techniques commonly employed in empirical finance research.

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.

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 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. 

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 is the final required credit course for the Academic Skills Center. The seminar is designed primarily to assist dissertation proposal workshop participants with their written research proposals and presentations via consultation with Academic Skills Center faculty. For DPW candidates, the seminar will work towards the first official DPW draft due November 1st (or when the SAO announces). Consultations will continue through DPW week. All students deliver a practice presentation of their research proposals prior to DPW week. Students not wishing to participate in DPW can complete the course requirements by participating in all elements of the course without final attendance at DPW.

The Research Methodology Seminar is a structured sequence of activities designed to guide second-year PhD students through the early stages of independent research. Its primary objective is to help students identify promising research questions, refine preliminary ideas into viable projects, and prepare effectively for the DPW. An information meeting will be held in the fall semester.

CS2 MA is a consultation-based course with one introductory lecture designed to support students writing a master’s paper in fulfillment of the US MA degree requirement at CERGE-EI. Enrollment in CS2 MA is a requirement for submitting an MA paper. The SAO will notify you if you are enrolled in CS2MA and will inform you of when the introductory lecture will be held.
Students should consult with both the ASC instructor and the Economics instructor on their plans for the paper in terms of type and proposed content early in the semester.
The papers are graded on the CS2MA course as per normal ASC writing standards. They must pass on the Economics side for content in order for an MA degree to be awarded.