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We study the driving forces of fluctuations in an estimated New Neoclassical Synthesis model of the U.S. economy with several shocks and frictions. In this model, shocks to the marginal efficiency of investment account for the bulk of fluctuations in output and hours at business cycle frequencies. Imperfect competition and, to a lesser extent, technological frictions are the key to their transmission. Labor supply shocks explain a large fraction of the variation in hours at very low frequencies, but are irrelevant over the business cycle. This is important because their microfoundations are widely regarded as unappealing.
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Not in an estimated DSGE model of the US economy, once we account for the fact that most of the high-frequency volatility in wages appears to be due to noise, rather than to variation in workers' preferences or market power.
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Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious naïve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach is theoretically grounded, easy to implement, and greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well both in terms of out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions.
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We use a quantitative equilibrium model with houses, collateralized debt and foreign borrowing to study the impact of global imbalances on the U.S. economy in the 2000s. Our results suggest that the dynamics of foreign capital flows account for between one fourth and one third of the increase in U.S. house prices and household debt that preceded the financial crisis. The key to these findings is that the model generates the sustained low level of interest rates observed over that period.
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The housing boom that preceded the Great Recession was due to an increase in credit supply driven by looser lending constraints in the mortgage market. This view on the fundamental drivers of the boom is consistent with four empirical observations: the unprecedented rise in home prices and household debt, the stability of debt relative to house values, and the fall in mortgage rates. These facts are difficult to reconcile with the popular view that attributes the housing boom to looser borrowing constraints associated with lower collateral requirements. In fact, a slackening of collateral constraints at the peak of the lending cycle triggers a fall in home prices in our framework, providing a novel perspective on the possible origins of the bust.
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The surge in credit and house prices that preceded the Great Recession was particularly pronounced in ZIP codes with a higher fraction of subprime borrowers (Mian and Sufi, 2009). We present a simple model with prime and subprime borrowers distributed across geographic locations, which can reproduce this stylized fact as a result of an expansion in the supply of credit. Due to their low income, subprime households are constrained in their ability to meet interest payments and hence sustain debt. As a result, when the supply of credit increases and interest rates fall, they take on disproportionately more debt than their prime counterparts, who are not subject to that constraint.
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