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Empirical Economics

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01.08.2014

Evaluating FOMC forecast ranges: an interval data approach

verfasst von: Henning Fischer, Marta García-Bárzana, Peter Tillmann, Peter Winker

Erschienen in: Empirical Economics | Ausgabe 1/2014

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Abstract

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve publishes the range of members’ forecasts for key macroeconomic variables, but not the distribution of forecasts within this range. To evaluate these projections, previous papers compare the midpoint of the range with the realized outcome. This paper proposes an alternative approach to forecast evaluation that takes account of the interval nature of projections. It is shown that using the conventional Mincer–Zarnowitz approach to evaluate FOMC forecasts misses important information contained in the width of the forecast interval. This additional information plays a minor role at short forecast horizons but turns out to be of sometimes crucial importance for longer-horizon forecasts. For 18-month-ahead forecasts, the variation of members’ projections contains information that is more relevant for explaining future inflation than information embodied in the midpoint. Likewise, when longer-range forecasts for real GDP growth and the unemployment rate are considered, the width of the forecast interval comprises information over and above the one given by the midpoint alone.

Vorheriger Artikel Real-time forecasting US GDP from small-scale factor models

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The Federal Reserve Board’s staff members produce their own set of forecasts collected in the Greenbook. These projections are point forecasts and are available to each FOMC member prior to the meeting. A separate strand of the literature analyzes the quality of Greenbook forecasts, see e.g. Romer and Romer (2000), Sims (2002), D’Agostino and Whelan (2008), Capistrán (2008), Gamber and Smith (2009), and Sinclair et al. (2010).

The field of density forecasts in economics is still far from having reached a mature state. For a brief overview of the relevant literature on predictive densities and the problem of how to evaluate their accuracy when the true density cannot be observed cf. Kascha and Ravazzolo (2010) and the references therein.

See also Reifschneider and Tulip (2007) and Rudebusch (2008) for this point.

Gavin and Pande (2008) find that the midpoint of the CT closely matches both the mean and the median of the distribution of all individual forecasts, which are the conventional measures of consensus among policymakers.

The spread might thus be considered an omitted variable in the usual Mincer–Zarnowitz regression as given by Eq. (1). Since the spread as a measure of uncertainty in the distribution of forecasts can be viewed as a nontrivial function of the midpoint, this notion is also in line with Ramsey (1969) who suggested adding nonlinear functions of the regressors as additional explanatory variables in order to test for specification errors.

Recently, individual forecasts are made available for a short sample period with a publication lag of ten years, see Romer (2010). Tillmann (2011) uses this new data set to uncover strategic forecasting behavior of FOMC members. Based on that data set, Banternghansa and McCracken (2009) study the degree of forecast disagreement among FOMC members.

Both the Bureau of Economic Analysis (BEA) and the Fed used GNP as the measure of real aggregate output until 1992, when they switched to GDP. As regards the inflation rate, the FOMC switched among several price indices in the past. From 1979 to 1988, the inflation rate forecasts were based on the change in the GNP deflator. In 1989, however, the committee switched to inflation based on the consumer price index (for all urban consumers), which was then replaced by the price index of (overall) personal consumption expenditures in 2000. The latter was interchanged with the price index of core personal consumption expenditures in July 2004. The FOMC started reporting inflation rate projections based on both the overall and the core PCE price index with the February 2008 MPR.

Since 2005, the forecasts in the February report also pertain to the next calendar year (24-month-ahead forecasts). Following the October 2007 meeting, the FOMC changed the frequency of forecasts, lengthened the forecast horizon to around 3 years, and raised the number of variables to be forecast. In addition, members are asked for their perception of forecast uncertainty. See Reifschneider and Tulip (2007) and Rudebusch (2008) for a discussion of these changes.

Gavin and Mandal (2003) attribute the relatively low degree of disagreement on a short-term point forecast for inflation to the FOMC members’ perceived lack of control over the inflation rate over horizons shorter than 18 months.

The columns headed “enhanced” refer to the the OLS regressions of model (5). We will turn to these results after analyzing those of the “simple” models.

Since the FR interval cannot have a smaller width than the truncated CT interval by construction, the FR spread automatically represents a degree of dispersion in the FOMC members’ individual views, which is at least as high as the one given by the CT spread. Hence, differences between the FR and the CT results can be attributed to the relevance of eliminating extreme views whereby the forecast dispersion is in general decreased beforehand. Note, however, that also the consensus forecast given by the midpoint might be altered when eliminating the six outliers.

González-Rodríguez et al. (2007, p. 69) provide a formal definition of the Hukuhara difference, together with an example of the condition guaranteeing its existence.

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Titel: Evaluating FOMC forecast ranges: an interval data approach
verfasst von: Henning Fischer
Marta García-Bárzana
Peter Tillmann
Peter Winker
Publikationsdatum: 01.08.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 1/2014
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-013-0736-z

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