We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples consisting of an artificial state-space...

This paper proposes a piecewise-linear Kalman filter (PKF) to estimate DSGE models with occasionally binding constraints. This method expands the set of models suitable for nonlinear estimation. It straightforwardly handles missing data, non-singularity (more shocks than observed time series),...

Indirect inference testing can be carried out with a variety of auxiliary models. Asymptotically these different models make no difference. However, in small samples power can differ. We explore small sample power with three different auxiliary models: a VAR, average Impulse Response Functions...