This paper develops a general perturbation methodology for constructing high-order approximations to the solutions of Markov-switching DSGE models. We introduce an important and practical idea of partitioning the Markov-switching parameter space so that a steady state is well defined. With this...

Markov-switching DSGE (MSDSGE) modeling has become a growing body of literature on economic and policy issues related to structural shifts. This paper develops a general perturbation methodology for constructing high-order approximations to the solutions of MSDSGE models. Our new method, called...

We estimate a Markov-switching mixture of two familiar macroeconomic models: a richly parameterized dynamic stochastic general equilibrium (DSGE) model and a corresponding Bayesian vector autoregression (BVAR) model. We show that the Markov-switching mixture model dominates both individual...

Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the "dynamic striated Metropolis-Hastings (DSMH)" sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from...