This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for "online" estimation, and...

We provide a toolkit for efficient online estimation of heterogeneous agent (HA) New Keynesian (NK) models based on Sequential Monte Carlo methods. We use this toolkit to compare the out-of-sample forecasting accuracy of a prominent HANK model, Bayer et al. (2022), to that of the representative...

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 -- an artificial state-space model, the...