This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process. For information on controlled access courses, including eligibility, application processes, deadlines, and departmental contact details, please refer to the Controlled Access Courses webpage.

New course for 2027/28 academic session.

Any external student must have successfully completed EC400 or EC451 in September and/or have approval from the Department of Economics.

Deadline for application: Please apply as soon as possible after the opening of course selection for all courses.

For queries, contact: Econ.msc@lse.ac.uk

Pre-requisites:

Students must have completed EC400 or EC451 before taking this course.

Additional requisites:

Students should also have completed courses in intermediate level macroeconomics and econometrics.

In exceptional circumstances, students may take this course without EC400 provided they meet the necessary requirements and have received approval from the course conveners, the MSc Economics Programme Director and their own Programme Director. Contact the Department of Economics for more information (econ.msc@lse.ac.uk) regarding entry to this course.

Empirical tools are becoming increasingly important in macroeconomics. This course provides the tools required to engage in macro-econometric analysis using time series data and cross-sectional identification using microeconomic data.

Why do aggregate variables such as unemployment, inflation and interest rates move over the business cycle? These questions are at the heart of modern macroeconomics. This course will study the empirical behaviour of the business cycle, with a focus on frontier methods such as causal inference, and answering macro questions with micro data.

This course is an applied course that provides students with the building blocks of empirical methods and an opportunity to gain hands-on experience in empirical analysis through formative and summative assessment.

Illustrative key methods:

• The data of macroeconomics
• Data analysis: detrending, cyclical adjustment, and others.
• Time series methods, VARs, Local Projections
• Forecasting and now-casting
• Identification in macroeconomics: Timing restrictions in VARs, high-frequency identification, narrative identification.
• State-space models: Kalman filters, time-varying parameters, and regime switching.
• Firm-Level Data and Investment Behaviour: Firm investment under uncertainty; capital
• adjustment costs; expectations data.
• Household Data and Consumption Dynamics: Using household-level data to study
• consumption and MPCs; linking micro and macro evidence.
• Exchange rate data, the challenge of forecasting exchange rates: tests of Uncovered Interest
• Parity and Covered Interest Parity.
• Measuring inflation expectations using financial and survey data.
• Textual Data, Machine Learning, and LLMs in Macroeconomics: Text-based measures of
• sentiment, uncertainty, and policy stance; topic models; supervised vs. unsupervised learning.
• Using LLMs to extract information from unstructured data; text embeddings for economic content; ethical and interpretive considerations.
• Inflation and the Phillips Curve
• Evidence on monetary and fiscal policy
• Micro movements in consumption
• Wage rigidity and unemployment fluctuations
• Secular movements in interest rates (“r-star”)

20 hours of lectures and 9 hours of seminars in the Autumn Term.

This course has a reading week in Week 6 of Autumn Term.

Two pieces of formative work will be assessed and written feedback provided. 

• Ash, Elliott, and Stephen Hansen. "Text algorithms in economics." Annual Review of Economics 15 (2023): 659-688.
• Canova, Fabio (2007). Methods for Applied Macroeconomic Research. Princeton University Press.
• Chodorow-Reich, Gabriel. "Geographic cross-sectional fiscal spending multipliers: What have we learned?." American Economic Journal: Economic Policy 11, no. 2 (2019): 1-34.
• Fernandez-Villaverde, Jesus (2026). Deep Learning for Solving Economic Models. Journal of Economic Literature.
• Huber, Kilian. "Estimating general equilibrium spillovers of large-scale shocks." The Review of Financial Studies 36, no. 4 (2023): 1548-1584.
• McKay, Alisdair, and Christian K. Wolf. "What can time‐series regressions tell us about policy counterfactuals?." Econometrica 91, no. 5 (2023): 1695-1725.
• Montiel Olea, José Luis, Mikkel Plagborg-Møller, Eric Qian, Christian Wolf (2025). Local Projections or VARs? A Primer for Macroeconomists, NBER Macroeconomics Annual 2025.
• Nakamura, Emi, and Jón Steinsson. "Identification in macroeconomics." Journal of Economic Perspectives 32, no. 3 (2018): 59-86.
• Stock, James H., and Mark W. Watson. "Identification and estimation of dynamic causal effects in macroeconomics using external instruments." The Economic Journal 128, no. 610 (2018): 917-948.

Project (100%, 6000 words).