Fernández-Villaverde, Jesús Hurtado, Samuel Nuño, Galo
Year of Publication:
CESifo Working Paper 8482
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.
heterogeneous agents wealth distribution financial frictions continuous-time machine learning neural networks structural estimation likelihood function