We study the relation between macroeconomic fundamentals and asset pricing through the lens of a dynamic stochastic general equilibrium (DSGE) model. We provide full-information Bayesian estimation of the DSGE model using macroeconomic variables and extract the time-series of four latent...

This paper provides a reverse mode derivative for DSGE models. Reverse mode differentiation enables the efficient computation of gradients from the model likelihood to the model parameters. These gradients can then be used by derivative based sampling algorithms including the No U-Turn Sampler....

This paper proposes a novel approach to estimate dynamic stochastic general equilibrium models in a liquidity trap. Our approach is based on the Monte Carlo particle filter and a self-organizing state space model. The main feature of this paper is that we estimate most parameters of DSGE models...

Existing methods for estimating nonlinear dynamic models are either highly computationally costly or rely on local approximations which often fail adequately to capture the nonlinear features of interest. I develop a new method, the discretization filter, for approximating the likelihood of...

We build up and estimate a two-region DSGE model of the Euro area, investigating the interactions between the peripheral countries (PIIGS) and the rest of EMU. Our main focus is on the 2008-2009 financial crisis and on the subsequent 2010-sovereign bond crisis. One striking result is that the...

This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a Sequential Monte Carlo filter proposed by Fernandez-Villaverde and Rubio-Ramirez (2004) and the Kalman filter. The Sequential Monte Carlo filter exploits the nonlinear structure of the...

This paper examines the forecasting performance of DSGE models with and without banking intermediation for the US economy. Over the forecast period 2001-2013, the model augmented with a banking sector leads to an improvement of point and density forecasts for inflation and the short term...

Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable...