Courses and Curriculum

This course provides an applied introduction to microeconomic analysis, with a focus on how economic models and data can be used to understand and inform decision-making in households, firms, and markets. Central to the course is the question: What determines prices, and how do agents respond to them?

The course examines consumer and producer (firm) behavior and explores how foundational economic models can be used to analyze real-world economic activity. Topics include consumer choice and demand, producer cost and supply, market structure and pricing, dynamics decision-making under uncertainty, and the role of information and incentives. The course also considers various market failures (e.g., due to externalities) and their implications for economic efficiency and government intervention.

Designed for students interested in applying economics tools in industry, consulting, technology, finance, or the public/nonprofit sectors, the course emphasizes both conceptual foundations and empirical relevance. Students will learn how to use microeconomic reasoning and data to address practical questions related to market strategy, pricing, resource allocation, and policy evaluation.
 

In today’s data-driven economy, professionals across industries must understand how to interpret macroeconomic trends, evaluate the health of economies, and respond strategically to changes in growth, employment, and inflation. This course equips students with the relevant tools, empowering them to understand and critically assess macroeconomic analyses from central banks, international institutions, and private-sector research teams.

The course is centered on the foundational macroeconomic models, with an emphasis on the neoclassical growth framework and its application to long-run trends in output, consumption, investment, and labor markets. It further examines the dynamics of income and wealth distributions, both within and across countries, and their patterns as related to structural changes and policy. The course also emphasizes real-world macroeconomic data—especially national income and product accounts (NIPA) data—and the interpretation of key indicators of economic performance.

Designed for students preparing for careers in industry, consulting, technology, finance, or the public/nonprofit sectors, the course bridges theory and practice, teaching students to think like economists in addressing real-world macroeconomic problems.
 

Modern applied economists must operate at the intersection of economics and data science. To that end, this course provides students with the foundational tools for empirical economic analysis, framed through the lens of modern predictive modeling, preparing students for advanced MAEDS coursework.

Topics include nonlinear and nonparametric regression; heteroskedasticity and serial correlation; simulation-based estimation and inference; model selection and cross-validation; ultra-high dimensionality; bias-variance tradeoffs, overfitting, and regularization; time-series issues including aspects of unobserved trend, seasonal, and cyclical components; extensibility and structural change; and forecast construction, evaluation, comparison, and combination. Students will distinguish between causal and non-causal prediction and work with cross-sectional, time-series, and panel data.

A strong emphasis is placed on building, assessing, and interpreting predictive models, with hands-on implementation using modern computing tools and environments.

This course builds computational and programming foundations essential for applied economics and data science. Emphasizing reproducibility, efficiency, and scalability, students gain hands-on experience with widely-used languages such as R or Python.

The course is structured around four main components:

1. Computational workflow and collaboration: Students learn development tools such as IDEs (e.g., RStudio, VS Code), version control (Git/GitHub), cloud environments (e.g., Jupyter, Quarto), documentation (Markdown, LaTeX), and reproducible research practices.
2. Programming for data science: Core concepts include control flow, modular design, piping/chaining (e.g., dplyr, pandas), object-oriented and functional programming, unit testing, debugging, and working with SQL databases—all with an emphasis on code quality.
3. Data acquisition and visualization: Students acquire, clean, and structure real-world data from files, APIs, and the web, and use advanced visualization tools (e.g., ggplot2, seaborn, plotly) to communicate insights effectively.
4. Additional tools: Examples include modern environments for simulation (e.g., simmer, Monte Carlo, simpy) and optimization (e.g., nloptr, scipy.optimize).

By course end, students will be equipped with a versatile computational toolkit and workflow skills suited for real-world data-driven roles in a wide variety of work environments.

This course builds on the foundations laid in Applied Microeconomics I (AEDS 6100) to explore more advanced topics in the design and operation of mechanisms and markets. Examples of important mechanisms include auctions, contracts, and voting systems; examples of important mechanism design considerations include incentives, the structure of information, and strategic interaction.

Markets are also a time-honored mechanism, so this course also examines in detail the structure and functioning of a key market, such as the labor market, where central issues include the determinants of labor supply and demand, equilibrium wage determination, disequilibrium dynamics, compensating wage differentials, human capital formation, migration, discrimination, and regulation.

Students will learn how to analyze mechanisms and markets from both theoretical and applied perspectives, with attention to efficiency, fairness, and practical implementation. Throughout, emphasis is on understanding how economists think about real-world mechanism and market design, and how the resulting insights can inform both business decisions and policy analysis.

This course builds on Applied Macroeconomics I (AEDS 6200) to examine advanced topics in macroeconomic policy, the behavior of firms and economies over the business cycle, and international linkages through trade and finance.

A primary topic is the design and impact of fiscal and monetary policy over the business cycle, with a focus on modern frameworks like the New-Keynesian DSGE (Dynamic Stochastic General Equilibrium) models. Related sub-topics include investment decisions in uncertain environments, addressing issues like sunk costs, option values, and macro-financial linkages.

The course also addresses international dimensions of macroeconomics, including global business cycle synchronization, trade and capital flows, exchange rate determination, and the causes and consequences of sovereign debt crises. Special attention is given to the role of international institutions such as the International Monetary Fund (IMF), the World Bank, the Bank for International Settlements (BIS), and the World Trade Organization (WTO) in shaping global outcomes.

Throughout the course, students will engage with current research, policy debates, and data-driven applications to develop a deeper understanding of how macroeconomics informs decision-making at the firm, national, and international levels.

This course builds on Econometrics I (AEDS 6300) to develop advanced tools for estimating and interpreting causal relationships, a critical ingredient for data-driven decision-making in a wide variety of business, government, and related environments.

The central focus in causal estimation is the problem of endogeneity, which arises when observed covariates (predictors) are correlated with unobserved factors, leading to biased estimates of the effects of interventions. The course explores why endogeneity occurs and how it can be addressed using a variety of methods, including instrumental variables, logistic and multinomial regression, panel data, and structural vector autoregressions.

Students will learn how to think critically about causality, identification, and interpretation, and put their knowledge to work via practical implementation in real-world contexts, such as evaluating policy interventions, analyzing marketing effectiveness, and modeling consumer and firm behavior in complex environments.

Machine learning (ML) has become an essential tool across a wide range of data-based business and government operations. This course provides an applied introduction to both supervised and unsupervised machine learning techniques, with a focus on their use in uncovering patterns, making predictions, and informing decisions in real-world economic settings.

Topics may include principal components and common factors; clustering; autoencoders; ridge regression; regularization as penalized regression; regularization via selection and/or shrinkage (e.g., LASSO); sample splits for model selection and evaluation; model averaging; "econometric-style" nonparametric regression (including series, kernel, nearest-neighbor, ...) vs "ML-style" nonparametric regression (including regression trees and forests, and potentially-deep neural networks (NNs)); capturing dynamics in NNs via feed-forward, long short-term memory, and transformer architectures; and methods for "penetrating the ML black box" to achieve performance attribution.

Throughout the course, students will explore real economic datasets, with a focus on interpretability, performance, and practical implementation.

This course uses tools from modern industrial organization to analyze firm behavior, strategic interaction, and market structure, with a focus on digital platforms and online marketplaces. Topics include demand estimation, pricing strategies, entry and platform competition, network effects, and algorithmic matching. Students will also explore the design and empirical evaluation of allocation mechanisms including auctions and centralized matching algorithms (e.g., Gale-Shapley). The course blends theoretical models (e.g., discrete choice, game-theoretic models) with structural estimation and reduced-form empirical techniques, preparing students to engage with policy questions in antitrust, regulation, and platform governance.

This course extends the analytical tools developed in the core Applied Macroeconomics sequence to evaluate the general equilibrium effects of macroeconomic policy interventions. Topics include monetary policy instruments such as interest rate adjustments, forward guidance, and large-scale asset purchases, as well as fiscal interventions like tax reforms, transfer programs, and countercyclical government spending. Emphasis is placed on identifying causal channels and quantifying policy impacts using both fully-specified structural dynamic stochastic general equilibrium (DSGE) models and reduced-form or semi-structural vector autoregressions (SVARs). The course emphasizes empirical calibration, estimation, and simulation-based policy evaluation.

This course examines climate change through the lens of empirical economic analysis, integrating tools from applied microeconometrics, macroeconometrics, and policy evaluation. Core topics include the bidirectional relationship between economic activity and climate outcomes, estimation of climate damages, and the economic valuation of mitigation and adaptation strategies. Students will engage with empirical representations of integrated assessment models (IAMs), examine sectoral case studies (e.g., energy, agriculture, finance), and apply econometric techniques to assess ESG metrics, climate risk stress testing, and insurance-based risk transfer. Datasets and estimation methods relevant to both public policy and private-sector decision-making are featured throughout.

This course presents a rigorous framework for estimating causal effects in observational and experimental settings using the potential outcomes model. The curriculum covers treatment effect heterogeneity, average treatment effects, and related estimands. Methodological coverage includes experimental methods (RCTs), quasi-experimental methods (e.g., instrumental variables, difference-in-differences, regression discontinuity designs), and modern machine learning approaches (e.g., causal forests, double machine learning). Students will use statistical software to replicate and extend empirical studies in applied fields such as labor economics, health policy, and environmental economics. Emphasis is placed on research design, identification strategies, and robustness analysis.

This course develops applied forecasting techniques for time series and high-dimensional panel data environments. Students will learn classical univariate and multivariate models (ARIMA, VAR), nonlinear extensions, and forecast combination methods. Coverage includes point, interval, and density forecasting, forecast evaluation under non-quadratic loss functions, and forecast uncertainty quantification. The course also treats big-data settings, including penalized regression (e.g., LASSO), factor models, and machine learning algorithms suitable for structured and unstructured data. Applications include macroeconomic nowcasting, financial risk modeling, and natural language-based forecasting. Students will work hands-on with large-scale datasets.

In this culminating course, students conduct applied research on a problem of relevance in a business, finance, or policy context using the empirical economic modeling toolkit acquired in the program. Working in teams, students design and execute a project end-to-end: formulating a research question, acquiring and cleaning data, selecting appropriate methodologies, estimating models, and delivering a final written report and oral presentation. Project options will be drawn from real-world challenges faced in the private and public sectors. Each project template will include guiding questions, suggested data sources, and recommended modeling strategies. Custom projects may be approved by the instructor. The course emphasizes empirical sophistication, research transparency, and the clear communication of economic insights to both technical and non-technical audiences.