We consider efficient methods for likelihood inference applied to structural models. In particular, we introduce a particle filter method which concentrates upon disturbances in the Markov state of the approximating solution to the structural model. A particular feature of such models is that...

Andrieu et al. (2010) prove that Markov chain Monte Carlo samplers still converge to the correct posterior distribution of the model parameters when the likelihood estimated by the particle filter (with a finite number of particles) is used instead of the likelihood. A critical issue for...

This paper explores the role of consumption habits using an estimated nonlinear dynamic stochastic general equilibrium (DSGE) model with heteroscedastic shocks. It finds that habits interact with time-varying volatility to produce a better and more plausible fit to the data. They accentuate the...

This article describes a new approximation method for dynamic stochastic general equilibrium (DSGE) models. The method allows nonlinear models to be estimated efficiently and relatively quickly with the fully-adapted particle filter. The article demonstrates the method by estimating, on US data,...

This paper examines the statistical properties of inflation in a sample of inflation-targeting and non-inflation-targeting countries. First, it analyses the time-varying volatility of a measure of the persistent component of inflation. Based on this measure, inflation-targeting countries...