Mar 26, 2021

Using Macroeconomic Modelling for Better Understanding of Policy Measures

Our recent online course on Macroeconomic Modelling for Open Economies allowed participants to learn how to use simple DSGE models of their national economies to estimate the impact of policy measures and changes in key macroeconomic variables. We were happy to talk to Mr. Eglent Kika, Head of Medium-Term Forecasting Unit at the Bank of Albania, about his experience and personal learning takeaways from this online course.

Where do you work and what motivated you to join the course on Macroeconomic Modelling for Open Economies?

I currently work as Head of the Forecasting and Strategy Sector at the Monetary Policy Department of the Bank of Albania, a position I have held since January 2021. Our strategic goal is to inform qualitatively and quantitatively the monetary policy decision-making process of the Supervisory Board, through providing medium-term forecasts and implementation strategies with respect to the inflation target.

We make use of two macroeconometric models in our Forecasting and Policy Analysis System (FPAS) and we are in the early stages of constructing of a DSGE model, which will hopefully help us with the long-run dynamics of the economy and evolution of trends, lacking in our current econometric infrastructure.

I regarded my participation in the Macroeconomic Modelling for Open Economies as a great opportunity to expand my knowledge of DSGE models, specifically their designs, application, idiosyncrasies, adaptabilities, and uses. Furthermore, it was extremely important for me to be able to benefit from the knowledge-sharing format on offer in the course and from the hands-on approach. I believe that “getting your hands dirty in the mud” with models, whereby you destroy and reconstruct them, and openly discussing relevant issues and challenges is the best way to acquire long-term knowledge and skills.

This course was an intensive training of technical skills that are required to develop and use a simple DSGE model of a national economy. Which new insights or ideas have you gained by participating in this course and how do you anticipate using them in your practice?

The course was intense, heavy on technical skills, involved a lot of coding, and made use of the whole Greek alphabet. On the technical nitty-gritty side, this was the primary reason why I was eagerly anticipating the participation in the course. It introduced a new dimension to all the accumulated non-technical knowledge I had of DSGE modelling. While I knew about the main principles of design, how to adapt it to small open economies, and introduce our economy’s idiosyncrasies, learning the technical side of implementation was my weakest link before the course.

Furthermore, I have always found the estimation strategy and the technical processes behind any small or large DSGE model to be very important but at the same time very delicate exercises. This course helped me in acquiring the right knowledge and technical skills not only to design a correct estimation strategy but also to be aware of the challenges and hurdles that I can face at any time in the implementation phase and to overcome them.

I consider all the technical skills that I acquired very important in my work in contributing to the expansion of the econometric toolkit available at the Bank of Albania. These skills will be imperative in completing the construction and full operationalization of the DSGE model that we have started to build. Additionally, I feel confident that I have gained the capabilities to introduce more details in the model that are specific to the Albanian economy, to be able to estimate the necessary parameters. Most importantly, now I feel much more at ease when explaining the model structure and interpreting its output to both a technical and non-technical audience.

How did you find the unit dedicated to consultation sessions as a supplement to the training? Did you find learning from other country examples of macroeconomic models valuable?

I found the consultation session as the natural climax of the course. For that, I sincerely thank the organizers for ensuring that it was impeccably planned and flawlessly executed, as it brought an earthly feel to all our experiences and challenges. It managed to blend very efficiently different country experiences with various economic specificities, policy challenges, modelling approaches, and, equally importantly, with different “stories” to tell. These elements fused all the technical and theoretical knowledge acquired in the course into individual presentations tailored to the presenter’s own country experience. I found it all extremely useful and a vital element of the course that gets you thinking beyond your own model’s status-quo and your challenges, and also warns you about potential issues that you may face in the future.

You also presented the challenges of your central bank in building the DSGE model. Did you find the advice of the lead expert useful for your further work?

In general, we the economic modellers and model executioners can get nerdy about the small details in the quest to achieve great perfection, at least the one we believe is true and exists conditional on the knowledge that we have at any moment in time. Because of this, we can spend a lot of time in trying to alter a DSGE model or introducing new features that we believe are extremely useful and vital to our work. Here, we are exposed to fallacy in the endeavor to examine the unknown. To avoid this, it suffices to consult or talk frankly with someone who was probably like you in the early days, but who, by now, has accumulated all the knowledge in the world to be safely called “the guru” of DSGE models.

This is exactly how I perceive the vital advice that I received from the lead expert during the consultation sessions that perfectly supplemented my own experiences with the DSGE model that we are constructing. Apart from getting a boost in confidence that we are on the right track and that the doubts we had on specific aspects of the model structure had to be examined further, I would also say that many of the things that I wished to do and incorporate in the model would have negligible impact on and minimal benefit for the big picture. This was a wake-up call for me to focus on the most efficient and productive aspects, either incomplete or missing in the model, and not inadvertently end-up “kick down the can” with details that will not add much to the end-product and that would probably waste a lot of useful time.