TARGET GROUP | Mid-level to senior officials responsible for monetary policy decision making and staff doing macroeconomic analysis and forecasting or operating macroeconomic models. Participants should have an advanced degree in economics or equivalent experience. It is highly recommended that applicants first take the Monetary Policy (MP) course and complete the online Macroeconometric Forecasting (MFx) course before applying for the MPAF. Participants should be comfortable using quantitative software such as EViews and Matlab/Octave, although specific knowledge of these is not required.
DESCRIPTION | This two-week course, presented by the IMF’s Institute for Capacity Development, provides rigorous training on the use of simple Dynamic New Keynesian (DNK) models to conduct monetary analysis and forecasting; it emphasizes analysis of monetary policy responses to macroeconomic imbalances and shocks. Participants are provided with the tools necessary to develop or extend the model to fit their own monetary policy framework. Country case studies are used to reinforce participant understanding and to help them compare and assess a variety of possible experiences.
OBJECTIVES | Upon completion of this course, participants should be able to:
• Customize a simple model of an economy that embodies the monetary policy transmission mechanism, and the shocks this economy may face
• Acquire and apply tools used in modern central banks to conduct monetary policy analysis and forecasting using a hands-on Matlab-based model
• Conduct nowcasting and near-term forecasting using a variety of estimation-based econometric techniques supported and expert judgment
• Use the model to develop consistent medium-term quarterly projections of such key macro variables as output, inflation, interest rate, and exchange rate
• Identify risks in the baseline forecast and draw up medium-term projections for alternative scenarios that assume that the risks materialize
• Start building a simple model for monetary policy analysis using their own national data, when they return home