Series: Working Papers. 2013.
Author: Jesús Fernández-Villaverde, Samuel Hurtado and Galo Nuño.
Topics: Inequality | Quantitative methods | Household finances | International Economy | Artificial intelligence and Big data.
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Abstract
We postulate a nonlinear DSGE model with a financial sector and heterogeneous
households. In our model, the interaction between the supply of bonds by the financial
sector and the precautionary demand for bonds by households produces significant
endogenous aggregate risk. This risk induces an endogenous regime-switching process
for output, the risk-free rate, excess returns, debt, and leverage. The regime-switching
generates i) multimodal distributions of the variables above; ii) time-varying levels of
volatility and skewness for the same variables; and iii) supercycles of borrowing and
deleveraging. All of these are important properties of the data. In comparison, the
representative household version of the model cannot generate any of these features.
Methodologically, we discuss how nonlinear DSGE models with heterogeneous agents
can be efficiently computed using machine learning and how they can be estimated with
a likelihood function, using inference with diffusions.