We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of...

In this chapter, both Maximum likelihood estimation (MLE) and Bayesian MCMC estimation methods are used to test their parameters estimation power while estimating a Markov-Switching generalized autoregressive conditional heteroscedasticity (MS-GARCH) model. The monthly exchange rates of BRICS...

We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...