This paper presents empirical evidence on the disagreement among Federal Open Market Committee (FOMC) forecasts. In contrast to earlier studies that analyze the range of FOMC forecasts available in the Monetary Policy Report to the Congress, we analyze the forecasts made by each individual member of the FOMC from 1992 to 1998. This newly available dataset, while rich in detail, is short in duration. Even so, we are able to identify a handful of patterns in the forecasts related to i) forecast horizon; ii) whether the individual is a Federal Reserve Bank president, governor, and/or Vice Chairman; and iii) whether individual is a voting member of the FOMC. Additional comparisons are made between forecasts made by the FOMC and the Survey of Professional Forecasters.

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This paper presents empirical evidence on the disagreement among Federal Open Market Committee (FOMC) forecasts. In contrast to earlier studies that analyze the range of FOMC forecasts available in the Monetary Policy Report to the Congress, we analyze the forecasts made by each individual member of the FOMC from 1992 to 1998. This newly available dataset, while rich in detail, is short in duration. Even so, we are able to identify a handful of patterns in the forecasts related to i) forecast horizon; ii) whether the individual is a Federal Reserve Bank president, governor, and/or Vice Chairman; and iii) whether individual is a voting member of the FOMC. Additional comparisons are made between forecasts made by the FOMC and the Survey of Professional Forecasters.

Droit de suite, Art, Art auctions, Price, Tax,

This paper presents empirical evidence on the efficacy of forecast averaging using the ALFRED (ArchivaL Federal Reserve Economic Data) real-time database. The authors consider averages over a variety of bivariate vector autoregressive models. These models are distinguished from one another based on at least one of the following factors: (i) the choice of variables used as predictors, (ii) the number of lags, (iii) use of all available data or only data after the Great Moderation, (iv) the observation window used to estimate the model parameters and construct averaging weights, and (v) the use of either iterated multistep or direct multistep methods for forecast horizons greater than one. A variety of averaging methods are considered. The results indicate that the benefits of model averaging relative to Bayesian information criterion-based model selection are highly dependent on the class of models averaged The authors provide a novel decomposition of the forecast improvements that allows determination of the most (and least) helpful types of averaging methods and models averaged across. Model averaging for forecasting is nothing new. An abundance of evidence suggests that model averaging can improve forecast accuracy relative to model selection. Empirical examples of this evidence include, but are certainly not limited to, Stock and Watson