Macroeconomists have long recognized that activity-gap measures are unreliable in real time and that this can present serious difficulties for stabilization policy. This paper investigates whether the credit-to-GDP ratio gap, which has been proposed as a reference point for accumulating countercyclical capital buffers, is subject to similar problems. We find that ex-post revisions to the U.S. credit-to-GDP ratio gap are sizable and as large as the gap itself, and that the main source of these revisions stems from the unreliability of end-of-sample estimates of the series' trend rather than from revised estimates of the underlying data. The paper considers the potential costs of gap mismeasurement. We find that the volume of lending that may incorrectly be curtailed is potentially large, although loan interest-rates appear to increase only modestly.

This paper presents a monetary DSGE model of the U.S. economy. The model captures the most important production, expenditure, and nominal-contracting decisions underlying economic data while remaining sufficiently small to allow it to provide a clear interpretation of the data. We emphasize the role of model-based analyses as vehicles for storytelling by providing several examples--based around the evolution of natural rates of production and interest--of how our model can provide narratives to explain recent macroeconomic fluctuations. The stories obtained from our model are both similar to and quite different from conventional accounts.

This paper considers the "real-time" forecast performance of the Federal Reserve staff, time-series models, and an estimated dynamic stochastic general equilibrium (DSGE) model--the Federal Reserve Board's new Estimated, Dynamic, Optimization-based (Edo) model. We evaluate forecast performance using out-of-sample predictions from 1996 through 2005, thereby examining over 70 forecasts presented to the Federal Open Market Committee (FOMC). Our analysis builds on previous real-time forecasting exercises along two dimensions. First, we consider time-series models, a structural DSGE model that has been employed to answer policy questions quite different from forecasting, and the forecasts produced by the staff at the Federal Reserve Board. In addition, we examine forecasting performance of our DSGE model at a relatively detailed level by separately considering the forecasts for various components of consumer expenditures and private investment. The results provide significant support to the notion that richly specified DSGE models belong in the forecasting toolbox of a central bank.

DSGE models are a prominent tool for forecasting at central banks and the competitive forecasting performance of these models relative to alternatives--including official forecasts--has been documented. When evaluating DSGE models on an absolute basis, however, we find that the benchmark estimated medium scale DSGE model forecasts inflation and GDP growth very poorly, although statistical and judgmental forecasts forecast as poorly. Our finding is the DSGE model analogue of the literature documenting the recent poor performance of macroeconomic forecasts relative to simple naive forecasts since the onset of the Great Moderation. While this finding is broadly consistent with the DSGE model we employ--ie, the model itself implies that under strong monetary policy especially inflation deviations should be unpredictable--a "wrong" model may also have the same implication. We therefore argue that forecasting ability during the Great Moderation is not a good metric to judge the usefulness of model forecasts.