Model-Based Monetary Policy Analysis and Forecasting (MPAFx)
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- Model-Based Monetary Policy Analysis and Forecasting (MPAFx)
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Bibliographic details
- Session: OL 24.180
- Location: Course conducted online
- Dates: May 1, 2024 - August 31, 2024 (18 weeks)
- Delivery method: Online Training
- Primary language: English
- Status: Deadline passed
Program overview
- Session No.: OL 24.180
- Location: Course conducted online
- Date: May 1, 2024 - August 31, 2024 (18 weeks)
- Delivery Method: Online Training
- Primary Language: English
- Presented by the Institute for Capacity Development
Qualifications and target audience
- Participants are expected to have a background in undergraduate macroeconomics, statistics, and econometrics.
- Participants are provided with guidance on how to access Matlab or Octave software.
- All government officials are welcome to register.
- The course is particularly well-suited for officials in central banks that are in the early stages of introducing Forecasting and Policy Analysis Systems (FPAS) with IMF assistance.
Course description and core coverage
- Introduces a semi-structural macroeconomic model often used as a core of FPAS at central banks.
- Demonstrates implementation of the key equations of a canonical quarterly projection model (QPM) in macroeconomic modeling software.
- Uses detailed country data highlighting an inflation targeting central bank for hands-on historical filtration, forecasting, and calibration exercises.
- Covers two main technical aspects:
- Introduction to a canonical New Keynesian model structure and its key properties.
- Implementation of the QPM in Matlab/Octave and application of IRIS toolbox for solving and maintaining the QPM, as well as forecasting with the QPM.
Learning objectives (upon completion 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.
- Produce a baseline forecast and alternative scenarios using the QPM.
References