Analytical Derivatives for Markov Switching Models

Methods for Markov-Switching Models

I develop a toolbox to analyze the statistical properties of multivariate Markov-switching models. I first derive analytical formulas for the evolution of first and second moments, taking into account the possibility of regime changes. The formulas are then used to characterize the evolution of expectations and uncertainty, the propagation of the shocks, the contribution of the shocks to the ov...

Markov-Switching Models

Bayesian Markov Regime-Switching Models for Cointegration

This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estima...

Markov-switching generalized additive models

We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for...

Explicit-Duration Markov Switching Models

Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time. The switching mechanism between regimes is controlled by unobserved random variables that form a first-order Markov chain. Explicit-duration MSMs contain additional variables that explicitly model the d...