Effective Learning through the Practical Application of Addressed Subject Matters
Jordan Simonov, Head of Forecasting and Analysis Unit at the Ministry of Finance of the Republic of North Macedonia, shares his story about the benefits attendance at the learning event that explored the question of how to perform effective and reliable public revenue forecasting analysis brought him. The event was delivered as part of the new thematic area within CEF learning program: Data and Analysis for Designing Policies (DAP) which supports our constituency in providing high-quality information for policy and decision-making, ensuring effective on-the-job application of economic concepts, tools and models, and carrying out strong analysis to support policy development and decision-making.
"In January 2019, I attended the latest workshop in the series on revenue forecasting, organized during 2017 and 2018, which were enabled by the Data and Analysis for Designing Policies (DAP) program.
The workshop discussed the theoretical basis and practical approach used in revenue forecasting. Interactive lectures helped me learn in detail about the methods of revenue planning used in SEE countries.
The emphasis was on practical exercises that included tax revenue forecasting, using the tax elasticity approach with the help of econometric models (Error Correction Model, Autoregressive Distributed Lag Model, etc.). These exercises were especially useful, because they gave me an opportunity to refresh my theoretical knowledge in this area, recollect what I have learned at the previous workshops, as well as to resolve some practical dilemmas in my work.
Upon completion of this three-day training, I came up with new ideas about how to apply the acquired knowledge in my everyday work in order to make revenue forecasting faster and more effective. For that purpose, I have stayed in contact with the lecturers to confirm my assumptions about the practical application of the presented models. Regarding the simplification of their usage, I wrote a script of several hundred lines in the R programming language.
Today, I finally see the results! I conduct revenue forecasting in the new R model, instead of the already existing models in Excel. Consequently, I am applying an integrated approach in revenue forecasting, which has reduced the forecasting time from several hours to a few minutes, has decreased the margin of error, and has helped obtain more robust results.
This is how I applied the practical knowledge and positive energy I received at the workshop organized by the CEF to do something that is really useful and will help me significantly in the performance of my tasks. Therefore, I would like to encourage future participants to follow my example, and rather than forgetting their knowledge acquired at the workshops, to apply it in their work."