FOMC Consensus Forecasts

Since November 2007, the Federal Open Market Committee (FOMC) of the U.S. Federal Reserve has regularly published participants’ qualitative assessments of the uncertainty attending their individual forecasts of real activity and inflation, expressed relative to that seen on average in the past. The benchmarks used for these historical comparisons are the average root mean squared forecast errors (RMSEs) made by various private and government forecasters over the past twenty years. This paper documents how these benchmarks are constructed and discusses some of their properties. We draw several conclusions. First, if past performance is a reasonable guide to future accuracy, considerable uncertainty surrounds all macroeconomic projections, including those of FOMC participants. Second, different forecasters have similar accuracy. Third, estimates of uncertainty about future real activity and interest rates are now considerably greater than prior to the financial crisis; in contrast, estimates of inflation accuracy have changed little. Finally, fan charts-constructed as plus-or-minus one RMSE intervals about the median FOMC forecast, under the expectation that future projection errors will be unbiased and symmetrically distributed, and that the intervals cover about 70 percent of possible outcomes-provide a reasonable approximation to future uncertainty, especially when viewed in conjunction with the FOMC's qualitative assessments. That said, an assumption of symmetry about the interest rate outlook is problematic if the expected path of the federal funds rate is expected to remain low.

We investigate how central bank forecasts of GDP growth evolve through time, and how they are adapted in the light of official estimates of actual GDP growth. Using data for 1988–2005, we find that the Federal Open Market Committee (FOMC) has typically adjusted its forecast for growth over the coming four quarters by about a third of the unexpected component of estimated growth in the four quarters most recently ended. We were unable to find any clear signs of systematic errors in the FOMC's forecasts. UK data for 1998–2005 suggest that the Bank of England Monetary Policy Committee (MPC) did not adjust its forecasts in this way, and that there were systematic forecast errors, but the evidence from the latter part of the period 2001–5 tentatively shows a behaviour pattern closer to that of the FOMC, with no clear signs of systematic errors.

Evaluating FOMC forecasts

This paper examines asymmetry of the loss function of professional forecasters for output growth, inflation, and exchange rate forecasts, based on the survey of professional forecasts (SPF) data of three South Asian countries (India, Indonesia, and Singapore). Our results provide India’s unbiased output growth forecasts and under-predicted inflation and exchange rate forecasts; Indonesia’s broadly unbiased forecasts; and Singapore’s under-predicted output growth forecasts and unbiased inflation and exchange rate forecasts. Testing the rationality of all three countries’ SPF forecasts, we find that all are rational under an asymmetric loss function but not under a symmetric loss function.

Among other properties of earnings forecasts, Das and Saudagaran (1998) document a difference in analyst forecast accuracy between non-U.S. incorporated firms listed in U.S. capital markets and U.S. incorporated domestic firms. In this paper we replicate their analysis using an extended sample period (1984–1993) and a control sample of U.S. incorporated multinational firms and conclude that the results reported in Das and Saudagaran (1998) are not driven by their use of a control sample comprised of domestic firms nor are their results specific to the sample period used. We also extend their analysis to demonstrate that the differences in forecast accuracy documented in Das and Saudagaran (1998) do not extend to firms listed on the NYSE. This result that differences in analyst forecast accuracy between non-U.S. firms listed in the U.S. and U.S. firms vary by U.S. exchange has not been examined in prior research. More importantly, we extend Das and Saudagaran (1998) by documenting the existence of cross-country differences in analyst forecast accuracy among our sample of non-U.S. incorporated firms listed in U.S. markets. This difference extends to firms listed on all three U.S. stock exchanges represented in our sample (the NYSE, NASDAQ, and AMEX).

The average values of numerous forecasts about South African economic variables are often calculated to give a summary measure of all the various forecasters' views. This mean forecast, often termed the consensus forecast, is found to increasingly represent the information held by forecasters as the forecast horizon declines. The forecasts of Inflation and GDP growth rates are investigated in this paper. The mean forecast is found to systematically underpredict large actual outcomes and overpredict low actual outcomes at long forecast horizons, with the opposite behaviour being found at short horizons. Both mean forecasts are found to be Muthian rational at all horizons. Forecasts of growth in GDP become more accurate as the forecast horizon declines, although not monotonically. Forecasts of inflation do become monotonically more accurate. The mean forecast is shown to be more accurate at all horizons compared to two extrapolation models. No relationship is found between the degree of dispersion of the forecasts around the mean and the means's accuracy for forecasts of both GDP growth and inflation.

We analyze forecasts of consumption, nonresidential investment, residential investment, government spending, exports, imports, inventories, gross domestic product, inflation, and unemployment prepared by the staff of the Board of Governors of the Federal Reserve System for meetings of the Federal Open Market Committee from 1997 to 2008, called the Greenbooks. We compare the root mean squared error, mean absolute error, and the proportion of directional errors of Greenbook forecasts of these macroeconomic indicators to the errors from three forecasting benchmarks: a random walk, a first-order autoregressive model, and a Bayesian model averaged forecast from a suite of univariate time-series models commonly taught to first-year economics graduate students. We estimate our forecasting benchmarks both on end-of-sample vintage and real-time vintage data. We find that Greenbook forecasts significantly outperform our benchmark forecasts for horizons less than one quarter ahead. However, by the one-year forecast horizon, typically at least one of our forecasting benchmarks performs as well as Greenbook forecasts. Greenbook forecasts of the personal consumption expenditures and unemployment tend to do relatively well, while Greenbook forecasts of inventory investment, government expenditures, and inflation tend to do poorly.

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.

This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated US recessions. We generate forecasts from six different models of the US economy and compare them to professional forecasts from the Federal Reserve’s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates.

We estimate a simple reduced-form model of expectation formation with three distinct deviations from full-information rational expectations that combines underreaction with overreaction to new information. In particular, forecasts are sticky, extrapolate the most recent news about the current period, and depend on the lagged consensus forecast about the period being forecast. We find that all three biases are present in the Survey of Professional Forecasters as well as in the Livingston Survey, and that their magnitudes depend on the forecasting horizon. We also stress the point that using the past consensus forecast to form expectations is a reasonable thing to do if a forecaster is not able to come up with full-information rational expectations all by herself. Finally, we show that forecasters that have more sticky expectations generally tend to rely less on the lagged consensus forecast but extrapolate news about the current period more.

The Federal Open Market Committee’s inflation target is stated in terms of the personal consumption expenditures price index (PCEPI). The PCEPI, like the consumer price index (CPI), measures inflation in the expenditures of households, but these indexes differ in purpose, scope, and construction. Notably, since the CPI is used as the reference rate for numerous financial contracts, one can derive implied longer-run CPI inflation forecasts from financial contracts. Such forecasts are widely reported. But if policymakers are to use these forecasts to guide their pursuit of the inflation target, they need to translate these CPI inflation forecasts into corresponding implied PCEPI forecasts. Since 1978, CPI inflation has averaged 0.3 percentage points above PCEPI inflation, but this differential has varied significantly over time. In this Commentary, we explain why, investigate a key historical episode, and provide an updated estimate of the likely differential going forward.

Is monetary policy important for forecasting real growth and inflation?

It is well known that, even if all forecasters are rational, unbiasedness tests using consensus forecasts are inconsistent because forecasters have private information. However, if all forecasters face a common realization, pooled estimators are also inconsistent. In contrast, we show that when predictions and realizations are integrated and cointegrated, microhomogeneity ensures that consensus and pooled estimators are consistent. Therefore, contrary to claims in the literature, in the absence of microhomogeneity, pooling is not a solution to the aggregation problem. We reject microhomogeneity for a number of forecasts from the Survey of Professional Forecasters. Therefore, for these variables unbiasedness can only be tested at the individual level.

Forecast combination through dimension reduction techniques

Given that most management forecasts in recent years are issued in range form and that most firms are followed by multiple analysts issuing dispersed forecasts, the conventional measure of management forecast news based on the midpoint of management forecast and the consensus analyst forecast does not fully capture the information content conveyed by range forecasts. This paper seeks to partially uncover such information by investigating an unexamined dimension unique to range forecasts –– overlapping between management forecast ranges and the ranges formed by individual analyst forecasts, which occurs in nearly 84% of range forecasts. Controlling for the width of management forecasts and analyst forecast dispersion, we find that compared with “overlap” management forecasts, “non-overlap” forecasts have greater impact on analyst revisions and stock market reaction to per unit forecast news. The non-overlap forecasts also turn out to have high relative forecast accuracy over analyst consensus forecasts. Moreover, individual analysts whose prior forecasts are outside of management forecast ranges respond more strongly to “non-overlap” forecasts than to “overlap” forecasts, and this difference is significant only when management forecasts convey bad news. Overall, our findings suggest that range forecasts convey more information beyond just the midpoint and analysts’ consensus.