RA-GAP VAT Gap Estimation Model (VGAPx)
Source details
- Canonical URL
- RA-GAP VAT Gap Estimation Model (VGAPx)
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Bibliographic details
- Session: OL 26.227
- Location: Course conducted online
- Dates: May 1, 2026 - April 15, 2027 (50 weeks)
- Delivery method: Online Training
- Primary language: English
- Status: Apply online by April 1, 2027 Deadline extended
Training program and target audience
- Training program: RA-GAP VAT Gap Estimation Model.
- Session No.: OL 26.227.
- Target audience: Officials in ministries of finance and revenue administrations involved in tax gap and revenue performance analysis.
- Presented by: Fiscal Affairs Department.
Logistics and delivery
- Location: Course conducted online.
- Date: May 1, 2026 - April 15, 2027 (50 weeks) New dates.
- Delivery Method: Online Training.
- Primary Language: English.
- Application status: Apply Now (course offers online training to government officials and the public).
Participant qualifications and technical requirements
- Expected familiarity: strong familiarity with government finances, value-added tax (VAT) design, and at least an intermediate level of familiarity with national accounts.
- Technical skills: Intermediate Microsoft Excel skills.
- Equipment and access: access to a computer with a reliable Internet connection with a Google Chrome web browser are essential.
Course description and structure
- Objective: provides instruction on how to prepare and execute VAT gap estimation model (VGEM) of the IMF’s Revenue Administration Gap Analysis Program (RA-GAP).
- Modular structure: course is broken into five modules covering:
- an overview of the VAT gap modeling framework;
- using the VAT gap estimation model;
- measuring actual VAT;
- constructing the potential VAT base;
- running the model, interpreting the results, and troubleshooting.
Course objectives (learning outcomes)
Upon completion of the course, participants should be able to:
- Prepare the data inputs for VGEM.
- Produce and interpret the results from VGEM.
- Diagnose and correct for errors that might be effecting the results.
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