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Monetary and Fiscal Policy Analysis with DSGE Models

Virtual : Monetary and Fiscal Policy Analysis with DSGE Models (DSGE)

Invitation

Session No.: CT 21.07V

Location: Beijing, China

Date: August 23, 2021 - September 3, 2021 (2 weeks)

Primary Language: English

Interpretation Language: Chinese

    Target Audience

    Mid-level to senior officials who use Dynamic Stochastic General Equilibrium (DSGE) models in the macroeconomic analysis of monetary and fiscal policy issues.

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    Qualifications

    Participants are expected to have an advanced degree in economics or equivalent experience, solid quantitative skills, and a basic knowledge of MATLAB/Octave and Dynare/Iris. It is strongly recommended that applicants have completed the online Macroeconometric Forecasting (MFx) course.

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    Course Description

    This course, presented by the Institute for Capacity Development, deals with building, using, and interpreting DSGE models. It introduces participants to the models and techniques that policy makers commonly use in analyzing monetary and fiscal issues. The course devotes a large number of lectures to model design and implementation issues and uses case studies relevant to the region to illustrate how these models are applied and how they can contribute to the policymaking process. The course discusses the advantages and limitations of the models when they are used for policy analysis and advice.

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    Course Objectives

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

    • Describe the models and techniques (computation and estimation) that policy makers use in analyzing monetary, fiscal, and structural issues.
    • Build a basic DSGE model from first principles using data for a country case in the region.
    • Augment or modify the model structure to address an economic policy question.
    • Apply the DSGE models developed in the course to various policy questions and interpret their results.
    • Identify the advantages and limitations of the models when used for policy analysis and advice.
    • Start building a model based on their own country’s data.
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