Many people engaged in activities related to business, financial markets, and policymaking closely follow economic forecasts. Our interest in forecasts stems from the fact that, to an important degree, the decisions we make today are influenced by our expectations about the economy. Accurate forecasts lead to better decision-making and more efficient use of economic resources, and so there is a clear benefit to identifying good forecasts.
An important resource for evaluating the predictions and performance of professional forecasters is the Survey of Professional Forecasters, conducted by the Philadelphia Fed Research Department’s Real-Time Data Research Center. The SPF is a quarterly survey that asks a panel of professional forecasters about their projections for a range of economic variables, including output growth, unemployment, inflation, and interest rates. When examining the SPF data, it becomes clear that professional forecasters have wide-ranging views about the future evolution of the economy. This is perhaps a bit surprising, since the statistical methods that underlie good forecasting models are well known, and professional forecasters by and large have access to the same data on the economy’s past performance.
With forecasters having similar tools and data to work with, why do we observe this wide dispersion in their projections?1 Are expectations wide-ranging because of differences in models and methods used to make the forecasts? Or does the wide disagreement stem from how different forecasters process and analyze information and then use it as an input into their forecast-generation process? To design and implement effective economic policies, it is important to understand how expectations are formed. One way to do so is to study forecast disagreement. in this article we will examine some features of the forecasts that underlie the SPF and discuss what theories and evidence tell us about forecaster behavior and how expectations about the economy are formed and evolve over time.
This article appeared in the Second Quarter 2014 edition of Business Review. Download and read the full issue.
For a discussion on measuring the accuracy of the survey’s forecasts, which is beyond the scope of this article, see Stark (2010).