RDP 2017-01: Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors: The Federal Reserve's Approach 2. Motivation for Publishing Uncertainty Estimates

Many central banks provide quantitative information on the uncertainty associated with the economic outlook. There are several reasons for doing so. One reason is to help the public appreciate the degree to which the stance of monetary policy may have to be adjusted over time in response to unpredictable economic events as the central bank strives to meet its goals (in the case of the FOMC, maximum employment and 2 percent inflation). One way for central banks to illustrate the potential implications of this policy endogeneity is to publish information about the range of possible outcomes for real activity, inflation, and other factors that will influence how the stance of monetary policy changes over time.

Publishing estimates of uncertainty can also enhance a central bank's transparency, credibility, and accountability. Almost all economic forecasts, if specified as a precise point, turn out to be ‘mistakes’ in the sense that outcomes do not equal the forecasts. Unless the public recognizes that prediction errors – even on occasion quite large ones – are a normal part of the process, the credibility of future forecasts will suffer and policymakers may encounter considerable skepticism about the justification of past decisions. Quantifying the errors that might be expected to occur frequently – by, for example, establishing benchmarks for ‘typical’ forecast errors – may help to mitigate these potential communication problems.

Finally, there may be a demand for explicit probability statements of the form: ‘The FOMC sees a 70 percent probability that the unemployment rate at the end of next year will fall between X percent and Y percent, and a Z probability that the federal funds rate will be below its effective lower bound three years from now’. Information like this can be conveniently presented in the form of fan charts, and we provide illustrations of such charts later in the paper. However, as we will discuss, the reliability of any probability estimates obtained from such fan charts rests on some strong assumptions.

For many policymakers, the main purpose of providing estimates of uncertainty is probably straightforward – to illustrate that the outlook is quite uncertain and monetary policymakers must be prepared to respond to a wide range of possible conditions.^[3] If these are the only objectives, then using complicated methods in place of simpler but potentially less-precise approaches to gauge uncertainty may be unnecessary; moreover, more complicated methods may be counterproductive in terms of transparency and clarity. The value of simplicity is reinforced by the FOMC's practice of combining quantitative historical measures with qualitative judgments: Under this approach, quantitative benchmarks provide a transparent and convenient focus for comparisons.

For these reasons, the estimates discussed in this paper and reported in the Summary of Economic Projections are derived using procedures that are simpler than those that might appear in some academic research. For example, we do not condition the distribution of future forecasting errors on the current state of the business cycle or otherwise allow for time variation in variance or skew, as has been done in several recent studies using vector autoregressive models or structural DSGE models.^[4]

However, ‘simple’ does not mean ‘unrealistic’. To be relevant, benchmarks need to provide a reasonable approximation to the central features of the data. Accordingly, we pay careful attention to details of data construction and compare our estimates and assumptions to recent forecast experience.

Footnotes

See Yellen (2016) and Mester (2016). For a look at a range of policymakers' views about the potential advantages and disadvantages of publishing information on uncertainty, see the discussions of potential enhancements to FOMC communications as reported in the transcripts of the January, May, and June 2007 FOMC meetings. (See www.federalreserve.gov/monetarypolicy/fomchistorical2007.htm). During these discussions, many participants noted the first two motivations that we highlight. In contrast, only one participant – Governor Mishkin at the January 2007 meeting – observed that financial market participants might find the publication of quantitative uncertainty assessments from the FOMC helpful in estimating the likelihood of various future economic events. [3]

For examples of the former, see Clark (2011), D'Agostino, Gambetti and Giannone (2013), and Carriero, Clark and Marcellino (2016); for examples of the latter, see Justiniano and Primiceri (2008) and Diebold, Schorfheide and Shin (2016). [4]