Credits – 3

Investigation of several important models of economic activity. Emphasis on methods of analysis and interpretation involving construction of mathematical models reflecting the economic substance of these models. Implications for economic policy considered.

Credits – 3

Students will acquire essential skills for transforming data into meaningful visualizations and performing
data analysis using Python. Emphasizing the creation of clear and elegant graphs from data, students will
delve into the art of exploratory data visualization. Additionally, the course covers regression analysis and
fundamental classification methods, demonstrating their real-world applications through hands-on
experience with real data sets.

Credits – 3

Measurement of micro- and macro-economic relations, both static and dynamic. Comparative statics and dynamics; practical use of inference from non-experimental data. Identification and estimation problems.

Credits – 3

This course explores the convergence of machine learning and causal inference, equipping students with the necessary skills to leverage the power of machine learning while investigating
causal relationships in their analyses. It encompasses core machine learning techniques such as model selection, prediction, tree-based classification, and neural networks. 

This course provides students with practical skills for conducting empirical econometric analysis in applied microeconomic research. It covers topics such as linear regression, panel data methods, instrumental variables, and models for discrete choice and limited dependent variables. Students will learn to apply established and state-of-the-art statistical methods to real-world economic data.

Credits – 3

This course bridges economic theory, statistics/econometrics, and practical data analysis in economics and
business. It emphasizes hands-on experience analyzing real-world individual-level survey data like the
American Community Survey and Panel Study of Income Dynamics, as well as aggregate data from agencies
like BEA, BLS, and FRED, focused on U.S. public policy and business.

Credits – 3

This applied course introduces empirical analysis of the global economy in international economics,
covering international trade (goods/resources transactions) and international finance (monetary/financial
transactions). Key topics include macroeconomic accounting via balance-of-payments, foreign exchange
markets, parity conditions, international monetary systems, determinants of international trade flows with
gravity analysis, and current issues like the U.S. current account and China’s rise.

Credits – 3

Applied Development Economics course covers key areas like health, education, social safety nets,
corruption, and conflict in low- and middle-income countries. It emphasizes rigorous research
methodologies, data analysis, and analytical skills to understand and analyze development issues. The
course aims to familiarize students with current research frontiers and equip them with tools to draw
credible inferences about the world.

Credits – 3

The course equips students with skills for empirical econometric analysis and modeling using time series
and financial data. Building on time series econometrics concepts, it focuses on applying established and
cutting-edge statistical methods to real-world financial and economic datasets. Topics covered include
autoregressive models, moving average models, vector autoregressive models, modeling volatility, asset
pricing, and risk management.

Credits – 3

The course Industrial Organization studies how imperfectly competitive markets function, focusing on the
causes and consequences of firms’ strategic behavior in such markets, as well as the role of public policies.
Topics covered include pricing strategies (price discrimination, algorithmic pricing, bundling), competition in
static and dynamic settings, collusion and cartels, horizontal mergers, vertical relationships, platforms and
the digital economy.