This course examines the development of positive models to understand the dynamics of key macroeconomic variables — employment, interest rates, output, and inflation — and applies these frameworks to evaluate government policy interventions. The course begins with the canonical Real Business Cycle (RBC) framework, assessing its capacity to account for established business cycle regularities before examining extensions that address its empirical limitations. 

Most of the course focusses on monetary economies, to study the transmission mechanism through which monetary policy affect real economic activity, inflation dynamics, and asset prices, examining both theoretical channels and related empirical evidence. The course concludes with an analysis of optimal fiscal and monetary policy design under commitment, based on the Ramsey approach.

The course will begin with an empirical overview of various dimensions of inequality as relevant to macroeconomics. The rest of the course will focus on three main areas. First, we will review the key quantitative heterogeneous agent frameworks, from Bewley/Huggett/Aiyagari models to HANK models. 

The second part of the course will cover housing and consumer de- fault from a macroeconomic perspective, reviewing the empirical evidence and some key quantitative models. The third part of the course will focus on labor market topics, such as the relation between the introduction of new technologies, employment outcomes and wages for different types of workers. 

Three-week course providing a review of the key mathematical methods

which will be used in the core courses for the Economics PhD program. Review of topics in real analysis, convex analysis, optimization, linear algebra, probability, and statistics. Any students planning on enrolling in the core economics courses are encouraged to also enroll in ECO 610.

An introduction to the analysis of dynamic models in economics. Topics in-

clude differential and difference equations, dynamic optimization, recursive methods and dynamic programming, and computational methods. While most applications in the class are in macroeconomics, other contexts will also be discussed as needed.

This course will serve as the first foray for graduate students into the probabilistic and statistical underpinnings of modern econometrics. Basics of probability theory, covering conditional probability, random variables, transformations, moments, cumulants, and asymptotic theory will be heavily explored. For statistical inference, the theory of sampling and estimation will be covered along with the key architecture necessary to conduct hypothesis testing and the construction of confidence intervals. The course will end with an introduction to the bootstrap and other modern simulation methods. 

This class will cover aspects of advanced applied econometrics, focusing

on the Bootstrap, Generalized Method of Moments (GMM) and Bayesian

econometrics with specific applications in efficiency analysis and economic growth. These methods will provide a sound foundation for microeconometric practice. The class will begin with an overview of GMM and then move on to various topics within the canon including overidentification,weak identification, misspecified moments and inference/clustering. This will be followed by a rigorous introduction to Bayesian econometrics with specific focus on model building and various sampling mechanisms through Gibbs and Metropolis-Hastings as well as direct sampling and importance sampling. Markov Chain Monte Carlo (MCMC) will also be introduced. The class will (hopefully) end with quasi-Bayesian GMM.

The goal of this course is to broaden students’ understanding of theoretical and applied econometrics that are mostly used in research. This is an advanced level course in econometrics with an assumed knowledge of statistical estimation, hypothesis testing as well as some knowledge of matrix algebra and calculus and other mathematical tools. Even though the course material will emphasize theoretical concepts, there will be ample opportunity to demonstrate knowledge using economic data in empirical applications.

This is the first of the two core microeconomics courses offered to incoming PhD students. The main focus of the course is an introduction to game and information theoretic tools. We will start with a brief discussion of individual choice which will serve as a building block to our analysis of strategic situations. 

This is the second of the two core microeconomics courses offered to incoming PhD students. The course focuses on strategic settings with incomplete information and payoff-relevant private signals. Topics include signaling, screening, Bayesian equilibrium concepts, and applications to contract and mechanism design. 

This course examines selected topics in dynamic macroeconomic theory with emphasis on quantitative and computational approaches. The material builds on the core dynamic models and develops tools for analyzing problems in economic growth, asset pricing, and fiscal policy. Particular attention is given to endogenous growth models of the Lucas-Romer type, the formation and valuation of speculative bubbles, and computational techniques for solving nonlinear general equilibrium models. The course concludes with applications to public debt and fiscal sustainability, highlighting how quantitative methods can be used to address contemporary macroeconomic issues. 

This course introduces PhD students to advanced empirical methods with applications to labor economics, public economics, household finance, and quantitative macroeconomics. These methods are divided into two main parts:

• Part I: Focuses on advanced econometric methods for causal inference, including instrumental variables, panel data methods (fixed effects and system GMM), difference-in-differences, and regression dis- continuity. Methods in this part can be implemented in either Stata  or R.

• Part II: Covers dynamic programming and quantitative analysis of structural models, including dynamic continuous choice, dynamic discrete choice, mixed discrete–continuous choice, and general equilibrium models. Implementation requires advanced programming languages such as Python, MATLAB, C/C++, or Fortran. 

Behavioral Economics: Foundations to Frontier

The course will first cover some of the most well-known biases in economics decision-making, including violations of expected utility, time discounting, and Bayesian updating. We will discuss both classic and recent experimental evidence, as well as some of the ways in which the deviations from standard theory can be modeled. This first part of the course will be lecture-based, with a reading list comprised of journal articles. The second part of the course will turn to the frontier literature on narratives and mental models in economics, first covering the main theoretical papers, and then turn to empirical evidence. The applied papers will vary from tightly-controlled lab experiments to field/survey evidence relating to macroeconomics, finance, health, religion, etc. Students can choose a paper to present most related to their area of interest. The final project will be to propose an experiment/field study/survey that incorporates mental models into a student’s area of interest. 

Empirical Industrial Organization:

Demand Estimation and Applications

This course introduces Ph.D. students to the empirical analysis of market structure, firm behavior, and competition policy. Emphasis is placed on econometric methods used to estimate demand systems for differentiated products and to analyze auction data. Students learn how to translate theoretical models into estimable empirical frameworks, implement structural estimation techniques, and evaluate the welfare implications of market outcomes. The course combines lectures, coding-based problem sets, and student presentations of seminal and recent research papers. The final project consists of a research proposal in industrial organization or related applied micro fields.