Financial Frictions and the Wealth Distribution

Abstract

This paper investigates how, in a heterogeneous agents model with financial frictions, idiosyncratic individual shocks interact with exogenous aggregate shocks to generate timevarying levels of leverage and endogenous aggregate risk. To do so, we show how such a model can be efficiently computed, despite its substantial nonlinearities, using tools from machine learning. We also illustrate how the model can be structurally estimated with a likelihood function, using tools from inference with diffusions. We document, first, the strong nonlinearities created by financial frictions. Second, we report the existence of multiple stochastic steady states with properties that differ from the deterministic steady state along important dimensions. Third, we illustrate how the generalized impulse response functions of the model are highly state-dependent. In particular, we find that the recovery after a negative aggregate shock is more sluggish when the economy is more leveraged. Fourth, we prove that wealth heterogeneity matters in this economy because of the asymmetric responses of household consumption decisions to aggregate shocks.

Economic Consequences of High Public Debt: Evidence from Three Large Scale DSGE Models

“…14 See, for example, Bacchiocchi and Missale (2005), Faraglia et al (2008), or Hatchondo and Martínez (2013).…”

“…8 See Codogno et al (2003), Attinasi et al (2010) and Corsetti et al (2013) for the effect on spreads and Laubach (2009), Baum et al (2012) for the effect on long-term sovereign interest rates. 9 See De Bonis and Stacchini (2013) for the impact on bank credit and Jorda et al (2016) for the effects in the aftermath of crises.…”

“…In case the debt-to-GDP ratio hits the debt limit, the country will default with probability one, and the debtor will face a haircut of around 30% (1-), thus retrieving only 70% () of full repayment. Following Corsetti et al (2013) and Batini et al (2018), the default probability t is described by a two-parameter distribution function (F):…”

“…The permanent component is common to all the members of the monetary union and captures the impact of a level change of the area-wide government debt-to-GDP ratio with respect to the initial level. 18 In the case of EAGLE we assume a normal distribution (like Darracq et al 2016 Source of the calibration: for EAGLE: Darracq et al 2016; GEAR: Corsetti et al (2013) and for BE: Batini et al (2018).…”

Real-time Weakness of the Global Economy: A First Assessment of the Coronavirus Crisis

“…For the sake of space, the estimated probability of low growth regime is plotted in Chart A of Figure 10 in the Online Appendix. The model attains a high probability of low growth only to the slowdown associated to the Great Recession, and fails to detect other periods of negative (2012)(2013) or weak (2001)(2002)(2003)(2004) output growth. This is because during the Great Recession, the Euro Area exhibited an unprecedented deterioration in real activity, which fully dominates the estimated low mean growth.…”