The effect of bank capital on lending is a critical determinant of the linkage between financial conditions and real activity, and has received especial attention in the recent financial crisis. We use panel regression techniques-following Bernanke and Lown (1991) and Hancock and Wilcox (1993, 1994)-to study the lending of large bank holding companies (BHCs) and find small effects of capital on lending. We then consider the effect of capital ratios on lending using a variant of Lown and Morgan's (2006) VAR model, and again find modest effects of bank capital ratio changes on lending. These results are in marked contrast to estimates obtained using simple empirical relations between aggregate commercial bank assets and leverage growth, which have recently been very influential in shaping forecasters' and policymakers' views regarding the effects of bank capital on loan growth. Our estimated models are then used to understand recent developments in bank lending and, in particular, to consider the role of TARP-related capital injections in affecting these developments.

Dynamic stochastic general equilibrium (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 mediumscale DSGE model forecasts inflation and GDP growth very poorly, although statistical and judgmental forecasts do equally 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. Although this finding is broadly consistent with the DSGE model we employ-the model itself implies that especially under strong monetary policy, 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 by which to judge models. D ynamic stochastic general equilibrium models were descriptive tools at their inception. They were useful because they allowed economists to think about business cycles and carry out hypothetical policy experiments in Lucas critique-proof frameworks. In their early form, however, they were viewed as too minimalist to be appropriate for use in any practical application, such as macroeconomic forecasting, for which a strong connection to the data was needed. The seminal work of Frank Smets and Raf Wouters (2003, 2007) changed this perception. In particular, their demonstration of the possibility of estimating a much larger and more richly specified DSGE model (similar to that developed by Christiano, Eichenbaum, and Evans 2005), as well as

Shifts in the long-run rate of productivity growth-such as those experienced by the U.S. economy in the 1970s and 1990s-are difficult, in real time, to distinguish from transitory fluctuations. In this paper, we analyze the evolution of forecasts of longrun productivity growth during the 1970s and 1990s and examine in the context of a dynamic general equilibrium model the consequences of gradual real-time learning on the responses to shifts in the long-run productivity growth rate. We find that a simple updating rule based on an estimated Kalman filter model using real-time data describes economists' long-run productivity growth forecasts during these periods extremely well. We then show that incorporating this process of learning has profound implications for the effects of shifts in trend productivity growth and can dramatically improve the model's ability to generate responses that resemble historical experience. If immediately recognized, an increase in the long-run growth rate causes long-term interest rates to rise and produces a sharp decline in employment and investment, contrary to the experiences of the 1970s and 1990s. In contrast, with learning, a rise in the long-run rate of productivity growth sets off a sustained boom in employment and investment, with long-term interest rates rising only gradually. We find the characterization of learning to be crucial regardless of whether shifts in long-run productivity growth owe to movements in TFP growth concentrated in the investment goods sector or economy-wide TFP.