Mixed-frequency VAR models with Markov-switching dynamics

Validating Markov Switching VAR Through Spectral Representations

We develop a method to validate the use of Markov switching models in modelling time series subject to structural changes. Particularly, we consider multivariate autoregressive models subject to Markov switching and derive close-form formulae for the spectral density of such models, based on their autocovariance functions and stable representations. Within this framework, we check the capabilit...

Estimating Markov and Semi-Markov Switching Linear Mixed Models with Individual-Wise Random Effects

We address the estimation of Markov (and semi-Markov) switching linear mixed models i.e. models that combine linear mixed models with individualwise random effects in a (semi-)Markovian manner. A MCEM-like algorithm whose iterations decompose into three steps (sampling of state sequences given random effects, prediction of random effects given the state sequence and maximization) is proposed. T...

Duration-Dependent Markov-Switching VAR Models with Applications to the Business Cycle Analysis

Hamilton (1989) introduced the Markov-switching models (from now on MS) to the business cycle researchers. He applied the MS model to the real U.S. GDP and showed how good the probability of being in recession or expansion generated by his model matched the NBER dating. Since then, the studies on business cycles that exploited the MS model have grown exponentially. Nevertheless, the basic MS mo...

Fads Models with Markov Switching Hetroskedasticity: decomposing Tehran Stock Exchange return into Permanent and Transitory Components

Stochastic behavior of stock returns is very important for investors and policy makers in the stock market. In this paper, the stochastic behavior of the return index of Tehran Stock Exchange (TEDPIX) is examined using unobserved component Markov switching model (UC-MS) for the 3/27/2010 until 8/3/2015 period. In this model, stock returns are decomposed into two components; a permanent componen...

Markov-Switching Models