State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth

It has been well documented that the consensus forecast from surveys of professional forecasters shows a bias that varies over time. In this paper, we examine whether this bias may be due to forecasters having an asymmetric loss function. In contrast to previous research, we account for the time variation in the bias by making the loss function depend on the state of the economy. The asymmetry parameter in the loss function is specified to depend on set state variables which may cause forecaster to intentionally bias their forecasts. We consider both the Lin–Ex and asymmetric power loss functions. For the commonly used Lin–Ex and Lin–Lin loss functions, we show the model can be easily estimated by least squares. We apply our methodology to the consensus forecast of real U.S. GDP growth from the Survey of Professional Forecasters. We find that forecast uncertainty has an asymmetric effect on the asymmetry parameter in the loss function dependent upon whether the economy is in expansion or contraction. When the economy is in expansion, forecaster uncertainty is related to an overprediction in the median forecast of real GDP growth. In contrast, when the economy is in contraction, forecaster uncertainty is related to an underprediction in the median forecast of real GDP growth. Our results are robust to the particular loss function that is employed in the analysis.

Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.

Corrigendum to “Least squares learning and the US treasury bill rate” [Econ. Syst. 38 (2014) 194–204]

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning.