Mohammad Alam

My research examines how monetary policy influences firm behavior, productivity, and resource misallocation, with a particular focus on supply-side channels that operate through investment, innovation, and capital adjustment. I also study firm dynamics, international trade, and the macroeconomic implications of policy shocks. A growing part of my work integrates machine learning and large-scale business datasets to improve causal inference, synthetic data generation, and forecasting. My ongoing projects explore the differential effects of monetary policy on misallocation, the accuracy of generative-AI-based inflation forecasts, and the role of sentiment dispersion, trade policy, and natural disasters in shaping economic outcomes.

I welcome motivated undergraduate and graduate students interested in monetary economics, applied macroeconomics, firm dynamics, and data-driven economic research. I offer research assistantship opportunities for students who wish to gain experience in empirical analysis, coding, and machine-learning applications in economics. I am also available to supervise graduate students working on topics related to monetary policy, productivity, misallocation, international trade, or AI-based economic modeling. Students with strong quantitative skills and experience in Python, R, Stata, or MATLAB are especially encouraged to reach out.

Alam, M. Jahangir. “Productivity Responses of High-Tech Firms to Monetary Policy.” North American Journal of Economics and Finance. (2025)

Alam, M. Jahangir, and Herb Emery. “Spatial Misallocation in Canada.” Review of Canadian Studies and Research. (2025)

Beasley, W. Jason, and M. Jahangir Alam. “Place-Based Scholarships: An Updated Assessment of Property Value Impacts from the Kalamazoo Promise.” Applied Economics. (2025)

Alvi, Eskander, and M. Jahangir Alam. “The Long-Run Effects of Monetary Policy: The Role of R&D Investment in Economic Growth.” Economic Modelling. (2024)

Alam, M. Jahangir. “Capital Misallocation: Cyclicality and Sources.” Journal of Economic Dynamics and Control. (2020)

Alam, M. Jahangir, Benoit Dostie, Jörg Drechsler, and Lars Vilhuber. “Applying Data Synthesis for Longitudinal Business Data across Three Countries.” Statistics in Transition. (2020)

• Advanced Macroeconomics (MA)
• Monetary Policy (PhD)
• Financial Econometrics (MA)
• Machine Learning in Economics (MA)
• Economics in Growth

I contribute to the AI Inflation Expectations project, a multi-institution research initiative evaluating the accuracy, reliability, and economic implications of inflation forecasts generated by large language models. This work is conducted in collaboration with colleagues at the Federal Reserve Bank of San Francisco (Shane Boyle, Huiyu Li) and Texas A&M University (Tatevik Sekhposyan). Our project website, which provides real-time forecasts and data resources, is available at: aiinflationexpectations.org/

Together, we analyze real-time AI-based inflation predictions, compare them with survey and market-based expectations, and assess how generative AI interprets macroeconomic conditions and policy signals. This research advances understanding of AI’s role in expectation formation, forecast evaluation, and macroeconomic information environments.

I am working on a multi-part research program with Matthew Schaffer examining the interaction between international monetary policy and global trade flows. The first study analyzes how U.S. Federal Reserve policy affects firms’ export–import activity using the Longitudinal Foreign Trade Transactions Database (LFTTD). The second investigates how U.S. monetary policy shocks propagate across countries, generating heterogeneous trade responses. The third project examines the spillover effects of European Central Bank (ECB) policy on trade between the United States and euro area economies, highlighting the role of cross-border monetary transmission.