This paper studies the role of credit supply factors in business cycle fluctuations using a dynamic stochastic general equilibrium (DSGE) model with financial frictions enriched with an imperfectly competitive banking sector. Banks issue collateralized loans to both households and firms, obtain funding via deposits, and accumulate capital out of retained earnings. Loan margins depend on the banks' capital-to-assets ratio and on the degree of interest rate stickiness. Balance-sheet constraints establish a link between the business cycle, which affects bank profits and thus capital, and the supply and cost of loans. The model is estimated with Bayesian techniques using data for the euro area. The analysis delivers the following results. First, the banking sector and, in particular, sticky rates attenuate the effects of monetary policy shocks, while financial intermediation increases the propagation of supply shocks. Second, shocks originating in the banking sector explain the largest share of the contraction of economic activity in 2008, while macroeconomic shocks played a limited role. Third, an unexpected destruction of bank capital may have substantial effects on the economy. Copyright (c) 2010 The Ohio State University.

and conference and seminar participants for comments and suggestions. A technical appendix containing additional results, a computational appendix and replication …les are available at the following web AbstractWe study sources and consequences of ‡uctuations in the housing market. The upward trend in real housing prices of the last 40 years can be explained by slow technological progress in the housing sector. Over the business cycle, housing demand and housing technology shocks explain one-quarter each of the volatility of housing investment and housing prices. Monetary factors explain 20 percent, but they played a bigger role in the housing cycle at the turn of the century. We show that the housing market spillovers are non-negligible, concentrated on consumption rather than business investment, and have become more important over time.

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The interaction between capital requirements and monetary policy is assessed by means of simple rules in a dynamic general equilibrium model featuring a banking sector. In “normal” times, when economic dynamics are driven by supply shocks, an active use of capital requirements generates modest benefits in terms of volatility of the target variables compared to the case in which only the central bank carries out stabilization policies. The lack of cooperation between the two policymakers may result in excessive volatility of the monetary policy rate and capital requirements. The benefits of introducing capital requirements become sizeable when financial shocks, which affect the supply of loans, are important drivers of economic dynamics; the availability of capital requirements as a policy tool yields a significant gain in terms of macroeconomic stabilization, regardless of the type of interaction between monetary and capital requirements policies.

4Non-technical summary 56 Concluding remarks 33Abstract This paper estimates the effects of technology shocks in VAR models of the U.S., identified by imposing restrictions on the sign of impulse responses. These restrictions are consistent with the implications of a popular class of DSGE models, with both real and nominal frictions, and with sufficiently wide ranges for their parameterers. This identification strategy thus substitutes theoretically-motivated restrictions for the atheoretical assumptions on the time-series properties of the data that are key to long-run restrictions. Stochastic technology improvements persistently increase real wages, consumption, investment and output in the data; hours worked are very likely to increase, displaying a hump-shaped pattern. Contrary to most of the related VAR evidence, resultsare not sensitive to a number of specification assumptions, including those on the stationarity properties of variables. JEL classification: C3, E3An important task of macroeconomics is to develop models that account for specific, quantitative features of the business cycle. Modern business cycle theory envisions a central role of random fluctuations in technological progress in driving the bulk of aggregate fluctuations. When technology shocks as volatile and persistent as estimated total factor productivity (TFP) are fed through a standard real business cycle (RBC) model, the simulated economy appears to be able to replicate the patterns of volatilities and cross-correlations of key macroeconomic time series of the postwar U.S. economy. This is a remarkable result for alternative, demand-driven theories have a much harder time in generating key business cycle facts like the strong unconditional procyclicality of both labor productivity and hours worked.The notion that technology shocks have anything to do with business cycles, however, has been recently questioned by a growing literature that aims at testing the predictions of the theory in terms of conditional moments in the data, i.e. conditional on technology shocks being the source of fluctuations, rather than the moments analyzed by RBC models. The key difficulty is that technology shocks need to be identified in the data. The seminal contribution by Galí [1999] originally identified technology shocks with time-series methods as the only source of long-run movements in labor productivity.His results show that a positive technology shock induces a fall in hours worked so persistent that a negative conditional correlation between output and hours worked ensues. Initially the literature reached conclusions similar to Galí [1999]. As stressed by this author, not only does this evidence, taken at face value, reject a key prediction of standard RBC theory, but it highlights a feature of the economy's response to aggregate technology shocks whose relevance goes beyond any specific macroeconomic paradigm. Because of the procyclicality of hours worked, some other shock(s) rather than technology shocks must be driving observed aggregate fluctuat...