The second issue of the scholarly quarterly Russian Journal of Money and Finance for 2024 has been published.
When making monetary policy decisions, central banks consider not only the expected deviation of inflation from the target but also the deviation of actual output from potential output. Potential output is the estimated level of output at natural rates of unemployment and capacity utilization that does not create inflationary pressure. However, potential output and the output gap are unobservable variables, and the statistics required to estimate them are released with a significant delay. This problem can be addressed by using regular business surveys. A study relying on the Bank of Russia’s monitoring of businesses proves that such surveys can be used to quickly estimate the output gap and adequately describe economic dynamics in the country.
Contrastingly, survey data on experts’ inflation expectations will not necessarily improve the accuracy of inflation forecasts. The analysis of Russian experts’ consensus forecasts shows that over most forecast horizons, consensus forecasts may lead to much more significant errors compared to traditional econometric methods. However, during periods of higher inflation, experts’ forecasts turn out to be more accurate than model-based ones as models adapt to rapid changes in the situation more slowly.
Inflation has always been a critical issue for central banks. The emergence of big data, including online shopping, and new ways to work with them, specifically machine learning methods among others, offer new opportunities to monitor price movements and forecast them. New approaches to measuring, analyzing, and forecasting prices were discussed in a workshop organized by the Bank of Russia, the New Economic School, and the Bank of Russia’s Department at HSE University. The review of this workshop is presented in the new issue.
The new issue of the Russian Journal of Money and Finance (No. 2, 2024) is available on their website.