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IMFx: Monetary Policy Analysis and Forecasting

Learn about the macroeconomic motivation of the quarterly projection model (QPM), its key properties, model calibration, data filtration, and how to implement the QPM in MATLAB software in order to learn and understand practical model building and model operation as it is usually done in central banks.

6 weeks
4–5 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

17,426 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Mar 29
Ends Apr 15

About this course

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This online course, presented by the IMF's Institute for Capacity Development, introduces participants to quarterly projection macroeconomic models developed as a core of FPAS (Forecasting and Policy Analysis Systems) and how to implement the key canonical quarterly projection model (QPM) equations in a macroeconomic modeling software. This course uses detailed country data highlighting an inflation targeting central bank, for hands-on filtration and calibration exercises.

The course covers two main technical aspects:

  • introduction to a canonical New Keynesian model structure and its key properties; and
  • implementation of the QPM in Matlab/Octave and the application of IRIS toolbox for solving and maintaining the QPM.

In partnership with MathWorks, enrolled students have access to MATLAB for the duration of the course.

Monetary Policy Analysis and Forecasting is offered by the IMF with financial support from the Government of Japan.

At a glance

  • Language: English
  • Video Transcript: English
  • Associated skills:Policy Analysis, Capacity Development, MATLAB, Monetary Policies, Forecasting

What you'll learn

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Upon completion of this course, participants should be able to:

  • Explain the key building blocks of a canonical semi-structural QPM.
  • Interpret the key model equations from a macroeconomic point of view.
  • Implement a simple QPM using a specialized software for macroeconomic modelling.
  • Distinguish the key elements of a QPM in a state-space form (i.e. shocks, observable and unobservable variables, measurement and transition equations, steady-state parameters, equation coefficients).
  • Identify necessary codes for data transformation, filtration and evaluation of the QPM properties.
  • Apply the basic IRIS Toolbox functions for solving the model.
  • Create output reports using model codes.
  • Develop a basic calibration of the QPM.

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