  Modern macroeconometrics often relies on time series models for which it is
time-consuming to evaluate the likelihood function. We demonstrate how Bayesian
computations for such models can be drastically accelerated by reweighting and
mutating posterior draws from an approximating model that allows for fast
likelihood evaluations, into posterior draws from the model of interest, using
a sequential Monte Carlo (SMC) algorithm. We apply the technique to the
estimation of a vector autoregression with stochastic volatility and a
nonlinear dynamic stochastic general equilibrium model. The runtime reductions
we obtain range from 27% to 88%.
