# 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.

## There is one session available:

After a course session ends, it will be archived.
Estimated 6 weeks
8–10 hours per week
Self-paced
Free

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

# What you'll learn

Skip What you'll learn

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.