Master quantitative models and techniques for tax revenue forecasting and policy analysis in this comprehensive IMF course.
Master quantitative models and techniques for tax revenue forecasting and policy analysis in this comprehensive IMF course.
This specialized course from the IMF Fiscal Affairs Department provides comprehensive training in revenue forecasting and tax policy analysis. Students learn essential quantitative modeling techniques for projecting government financial inflows, with a focus on tax revenues. The course covers data quality assessment, forecasting methodologies for various tax types, and techniques for analyzing policy changes' revenue and distributional impacts. Participants gain practical skills in using analytical tools for informed policy decisions.
4.5
(22 ratings)
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Instructors:
English
English
What you'll learn
Master fundamental principles of tax revenue analysis and forecasting
Understand effective institutional frameworks for revenue forecasting
Develop expertise in data requirements and quality assessment
Learn various revenue forecasting methodologies and their applications
Evaluate different forecasting methods' strengths and limitations
Gain skills in quantifying tax policy changes' revenue impact
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This comprehensive course focuses on revenue forecasting and tax policy analysis techniques. Students learn to access and assess data quality, apply general forecasting techniques to various tax types including VAT, import duties, excises, and income taxes. The curriculum covers institutional frameworks for revenue forecasting, quantitative modeling methods, and techniques for estimating policy change impacts. Special attention is given to practical applications using real-world scenarios and data analysis tools.
Fee Structure
Instructors
1 Course
International Tax Policy Expert and Economist
Sébastien Leduc is an Economist in the Tax Policy Division of the International Monetary Fund's Fiscal Affairs Department, a position he has held since 2016. Prior to joining the IMF, he served as a Senior International Tax Policy Economist at the Department of Finance Canada, where he focused on corporate income tax design and international taxation issues. At the IMF, Leduc leads multiple technical assistance activities and contributes to analytical work on international tax policy. His expertise spans areas such as corporate taxation, international tax cooperation, and revenue mobilization strategies for developing countries. Leduc has co-authored publications on topics including the impact of tax treaties on developing economies and alternative options for global revenue mobilization. His work contributes to the IMF's efforts in providing policy guidance on tax reforms that balance revenue generation with economic growth and inclusivity.
1 Course
Expert in Economic Modeling and Tax Policy Analysis
Dinar Prihardini is an Economist in the Tax Policy Division of the International Monetary Fund's Fiscal Affairs Department, specializing in macroeconomic modeling and fiscal policy analysis. Her expertise spans developing customized macro-CGE models for analyzing energy subsidy reforms, climate change policies, tax reforms, and labor market policies. Before joining the IMF, she worked at the World Bank where she developed economy-wide models for various countries, and prior to that, she held positions at the Australian Treasury and KPMG Australia's Economic Modelling Unit. Her research contributions include significant work on excess profit taxes, international taxation, and climate-related fiscal policies. Prihardini has co-authored numerous IMF working papers and publications, particularly focusing on tax policy reforms and their macroeconomic impacts across different economies. Her work combines technical modeling expertise with practical policy applications, making her a valuable contributor to international fiscal policy discussions.
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4.5 course rating
22 ratings
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