McKinsey: how to build accurate forecasts in the midst of an economic crisis

it is extremely important that the leader be able to quickly collect information and interpret it correctly. In this way, he will not only help his company survive, but also make it more resilient when the economy undergoes major changes or the post-crisis recovery begins. Therefore, leading organizations are increasingly using the method of ultra-short-term forecasting (English nowcasting). The impetus for its development was the dot-com crises and the 2008 recession, during which companies were overly dependent on standard economic data, which were often published with a delay (sometimes up to six months). As a result, they missed profitable opportunities and faced potential risks.

In ultra-short-term forecasting, organizations use sophisticated statistical analysis and up-to-date data from a wide range of sources. For example, ultra-short-term modeling has proven itself very well in determining future GDP growth rates: GDP data is usually published with a lag of several months, so without this technique, it is necessary to create dashboards to monitor the crisis and analyze scenarios using outdated data and subjective estimates. . This is fraught with a deterioration in the quality of decisions made and an increase in risks.

Thanks to ultra-short-term forecasting, companies can not only understand what is happening in the economy right now, but also what happened in the recent past. The methodology is particularly useful in cases where traditional models and intermediate indicators do not provide accurate estimates due to delays in the publication of data.

Many governments, financial institutions, and others use traditional ultra-short-term forecasting methods to understand the patterns of rapid economic change in the world and accurately predict the pace of economic recovery.

In many cases, these methods have done their job well. However, during the covid-19 crisis, Brexit, the US -China trade war , organizations faced unusual challenges: large-scale unforeseen events caused significant macroeconomic structural gaps in many relationships between economic indicators.