Economic effects of pandemic, impact of financial shocks on credit cycles, and advantages of news indices: new issue of Russian Journal of Money and Finance
The coronavirus pandemic has induced a recession that has become the deepest in decades. This has been mainly caused by restrictions imposed in various countries to combat the spread of infection. Using the input-output model, Aleksey Ponomarenko and his co-authors (the Bank of Russia) analyse how these measures have affected the Russian economy. They show that the slump in output associated with inter-industry relationships ultimately spills over to a wide range of industries and its scale may dramatically exceed the primary negative effect of the restrictions on companies’ operations in individual industries (e.g. in the service sector) and of decreased demand on Russian exports (e.g. oil exports).
The pandemic-related shock inevitably entails a reduction in the consumption and output of non-health goods. However, expansionary monetary and fiscal policies in these conditions involve specific risks. According to the model suggested by economists at the Central Bank of Armenia, stimulating policy supports consumption and overall economic activity that might simultaneously be connected with the spread of virus. In other words, as stressed by the authors, government authorities should seek a balance between supporting economic activity and protecting people’s health.
Financial shocks may trigger recessions, while recessions resulting in financial crises are generally deeper and longer-lasting. A team of authors from MGIMO, CERGE-EI in the Czech Republic, and CMASF analysed data on 27 advanced and emerging economies for the period from 1990 through 2019, to find out how various financial shocks affect changes in credit cycle phases in different countries. In particular, the authors assess the effect of various shocks in Russia in the course of the 2014–2015 crisis compared to the 2008–2009 crisis, emphasising that the Russian monetary authorities improved their capabilities to control credit cycle phases.
Economic activity indices based on survey findings (e.g. consumer sentiment and business confidence indices, etc.) have long been applied in macroeconomic forecasting, while being competed in recent years by indices based on information from the internet, such as news, search queries, and comments on social media. Filipp Ulyankin (RANEPA) analyses various types of indices, demonstrating that search and news indices built using internet data and machine learning methods predict main macroeconomic parameters better than indices based on surveys. Moreover, ‘automatic’ indices contain information making it possible to predict the movements of conventional survey-based indices.
To enhance the accuracy of domestic economy forecasts, it is necessary to have sufficiently precise forecasts of economic developments in other countries. Olga Korotkikh (the Bank of Russia) describes a model applied by the Bank of Russia’s Monetary Policy Department that enables coordinated forecasting for the three largest economies – the USA, China, and the euro area. According to the author, such simultaneous modelling makes it possible to take into account complex multi-country interactions, improving the accuracy of forecasting for Russia.
Online version of Russian Journal of Money and Finance, No. 4, 2020, is freely available here.