We develop a perturbational technique to approximate equilibria of a wide class of discrete-
time dynamic stochastic general equilibrium heterogeneous-agent models with complex …

We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities
and time variation for (possibly large sets of) macroeconomic and financial variables. From a …

This paper examines the alignment of inductive biases in machine learning (ML) with
structural models of economic dynamics. Unlike dynamical systems found in physical and …

This paper proposes a Differential-Independence Mixture Ensemble (DIME) sampler for the
Bayesian estimation of structural models. DIME allows the estimation of models that are …

MacroModelling. jl is a Julia (Bezanson et al., 2017) package for developing and solving
dynamic stochastic general equilibrium (DSGE) models. These kinds of models describe the …

In the long run, we are all dead. Nonetheless, even when investigating short-run dynamics,
models require boundary conditions on long-run, forward-looking behavior (eg …

In this chapter we revisit the forecasting performance of dynamic stochastic general
equilibrium (DSGE) models. We find, consistent with prior literature, that the forecasts from a …

I develop a population-based Markov chain Monte Carlo algorithm known as parallel
tempering to estimate dynamic stochastic general equilibrium models. Parallel tempering …

I prove that in any linearized general equilibrium model with existence and uniqueness of
equilibrium, any present response to a future shock always becomes weaker the further …