Does Data Confusion Equal Forecast Confusion?

Abstract

F orecasters, policymakers, and financial market partic-ipants closely scrutinize the regular economic data flows for signs about the economy's future strength. But there are many types of data. Some of these data mea-sure goods and services that are produced and consumed each month, while other types of data measure economic sentiment or financial market variables. Over the past few months, a noticeable discrepancy has developed between the types of data that forecasters and others regularly use to assess the strength or weakness of the U.S. economy, as measured by real GDP growth in the current quarter. One type of data is the regular flow of monthly data from government statistical agencies and other sources that are used in the construction of real GDP. Examples include key monthly series like real personal consumption expenditures (PCE), light vehicle sales, construction spend-ing, shipments of nondefense capital goods, business inven-tories, and exports and imports of goods and services. These types of data are often referred to as " hard " data. However, other types of data are also scrutinized for clues about the economy's health. These include data derived from surveys of businesses, consumer confidence and sentiment surveys, and financial market variables such as stock prices and the St. Federal Reserve Bank of St. Louis | research.stlouisfed.org the calculation of real GDP, but which are viewed as key indicators of economic health, are important labor market series such as the number of jobs added or subtracted each month, the unemployment rate, and initial weekly claims for state unemployment insurance benefits. These types of data are often referred to as " soft data. " 1 This essay provides two new index measures of " hard " and " soft " data that could be useful for quantitatively show-ing how different types of data can influence forecasts of real GDP and thus the expectations of policymakers. Figure 1 shows the monthly indexes of hard and soft eco-nomic data. The hard data index uses 16 data series and the soft data index uses 13 series. 2 The indexes exhibit the normal cyclical behavior one would expect in the data: They increase in expansions and decrease in recessions. 3 Figure 1 shows that early in the expansion, until late 2013 or so, there was a considerable gap between the hard and soft data; the hard data indicated stronger economic conditions than the soft data. However,