Economic forecasting has been called “the sole function of making astrology look respectable” by economist Ezra Solomon. However, as experts attempt to understand the effects of multiple shocks, including the pandemic and changing geopolitical conditions, this view is becoming increasingly outdated. Bank of England Governor Andrew Bailey recently faced questions about the central bank’s growth and inflation forecasts when they hiked rates by 25 basis points. However, economic forecasting is not an exact science and drastic corrections can damage the credibility of central bankers.
Central banks, including the US Federal Reserve and the European Central Bank, have made mistakes when forecasting inflation, particularly since the outbreak of the pandemic. The International Monetary Fund (IMF) has also revised its recent growth projections significantly. As forecasts inform investment decisions, household decisions, and policy-making, accuracy is essential. Policymakers’ forecast records also impact their credibility, with high confidence helping to anchor inflation expectations.
However, forecasts are conditioned on decisions made at a particular point in time and must be adjusted as new data arises. Thus, forecasts should be viewed as indicators rather than gospel truths. Central bank officials face an uncertain outlook in 2020 and beyond. They have had to take positions on epidemiology, war scenarios, supply chain changes, and rapidly evolving domestic and international policies. The limited public understanding of forecasts also contributes to false predictions.
Central bankers’ misjudgments about the effectiveness of fiscal stimulus, stability of inflation expectations, and the pandemic’s damage to supply have contributed to their delay in tackling inflation. Recent shocks emphasize the importance of leveraging cross-disciplinary expertise in economics and exploring how advances in big data, machine learning, and AI can improve economic analysis.
Improving how forecasts are communicated is also vital, particularly in uncertain times. Showing predictions under different scenarios helps to understand the range of possible outcomes. Communication of probability distributions on forecasts could also be enhanced. Furthermore, economists need greater clarity on how and why key judgments have changed between forecasts and how that affects the numbers. Achieving this may help build confidence in economic forecasts as reference points, rather than forward-looking predictions.
In conclusion, while economic forecasting is not an exact science, it is an essential tool for informing investment decisions, household decisions, and policy-making. Policymakers’ credibility depends on their forecast records, which impact inflation expectations. By communicating forecasts under different scenarios and demonstrating greater clarity, economists can improve confidence in their forecasts. As John Maynard Keynes famously said, “When the facts change, I change my mind. What is your occupation?”