Federal Reserve Forecasts: Asymmetry and State-Dependence

Caunedo, Julieta; DiCecio, Riccardo; Komunjer, Ivana; Owyang, Michael T.

doi:10.2139/ssrn.2232360

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…In another recent paper, Caunedo et al (2013) investigate the loss function based on Greenbook forecasts jointly for different macroeconomic variables. They find that the forecasts can be rationalized under asymmetric loss, whereas the degree of asymmetry depends on the phase of the business cycle.…”

Section: Introductionmentioning

confidence: 99%

Using Forecasts to Uncover the Loss Function of Federal Open Market Committee Members

2016

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We revisit the sources of the bias in Federal Reserve forecasts and assess whether a precautionary motive can explain the forecast bias. In contrast to the existing literature, we use forecasts submitted by individual Federal Open Market Committee (FOMC) members to uncover members' implicit loss function. Our key finding is that the loss function of FOMC members is asymmetric: FOMC members incur a higher loss when they underpredict (overpredict) inflation and unemployment (nominal and real growth) as compared to their making an overprediction (underprediction) of similar size. We also find that an asymmetric loss function, in some cases, weakens evidence against forecast rationality, though results depend on the variable being projected and the subgroup of FOMC members being studied. Furthermore, we add to the recent controversy on the relative quality of FOMC forecasts compared to staff forecasts. Our results suggest that differences in predictive ability could indeed stem from differences in preferences.

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Section: Introductionmentioning

confidence: 99%

Using Forecasts to Uncover the Loss Function of Federal Open Market Committee Members

2016

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“…A smaller number of studies have examined multivariate forecast rationality Komunjer & Owyang (2012). andCaunedo et al (2013) develop a methodology, applied to the Greenbook forecasts, that derives the weights of a forecaster's loss function Sinclair et al (2017). develop multivariate versions of forecast efficiency regressions and apply them to the Greenbook forecasts.…”

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confidence: 99%

Evaluating the conditionality of judgmental forecasts

Berge

Chang

Sinha

2019

International Journal of Forecasting

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We propose a framework to evaluate the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are faithfully incorporated into the remainder of the forecast. We consider whether the Greenbook, Blue Chip, and the Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest rate projections in these forecasts appear to be efficiently incorporated into forecasts of other economic variables.

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“…2 Evaluation methods assuming asymmetric loss functions are used to evaluate the rationality of EIA forecasts in for example Auffhammer (2007) and Mamatzakis and Koutsomanoli-Filippaki (2014). However, prior to this paper, methods which account for interdependence between forecasts, applied by Caunedo et al (2020) to evaluate forecasts produced by the Federal Reserve and by Bora et al (2021) to evaluate the forecasts of the U.S. Department of Agriculture, have yet to be used on EIA forecasts. Our emphasis is on modelling interdependence, as well as asymmetries, between the EIA forecasts of the key variables of world petroleum market.…”

Section: Introductionmentioning

confidence: 99%