Add to ORKGSince several decades, researchers have been interested in various types of generalized regres-sion models which admit changing parameter values at different time periods. The so-called regime switching models have given a lot of application in the fields of modelling of complex systems, robust identification, detection of behavior change and more generally in process diag-nosis. Here, we examine the case where the change in regime cannot be directly observed but may be estimated from observed variables (the input and the output of the process). For that purpose, the well known EM (expectation-maximisation) approach may be applied; to take into account the switches between the regimes, new variables (generally known as hidden or missing) ar...
Business cycle models are often investigated by using reduced form time series models, other than (o...
We consider state-space representation of a multivariate dynamic process with Markov switching in bo...
Regime Switching models, especially Markov switching models, are regarded as a promising way to capt...
It is well known that generalized-M (GM) estimators for linear models are consistent and lead to a s...
It is well known that GM estimators for linear models are consistent and lead to a small loss of eff...
The regime-switching Lévy model combines jump-diffusion under the form of a Lévy process, and Markov...
This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with reg...
The regime-switching GARCH model combines the idea of Markov switching and GARCH model, which also e...
Many psychological processes are characterized by recurrent shifts between distinct regimes or state...
Abstract — The paper describes an identification technique of switching system. The considered syste...
This article considers the estimation of dynamic exogenous switching regression models and dynamic e...
We propose a state space model with Markov switching, whose regimes are associated with the model pa...
This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with reg...
We propose an estimation method that circumvents the path dependence problem existing in Change-Poin...
Identification of switched systems has received considerable attention during the past few years. Th...
Business cycle models are often investigated by using reduced form time series models, other than (o...
We consider state-space representation of a multivariate dynamic process with Markov switching in bo...
Regime Switching models, especially Markov switching models, are regarded as a promising way to capt...
It is well known that generalized-M (GM) estimators for linear models are consistent and lead to a s...
It is well known that GM estimators for linear models are consistent and lead to a small loss of eff...
The regime-switching Lévy model combines jump-diffusion under the form of a Lévy process, and Markov...
This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with reg...
The regime-switching GARCH model combines the idea of Markov switching and GARCH model, which also e...
Many psychological processes are characterized by recurrent shifts between distinct regimes or state...
Abstract — The paper describes an identification technique of switching system. The considered syste...
This article considers the estimation of dynamic exogenous switching regression models and dynamic e...
We propose a state space model with Markov switching, whose regimes are associated with the model pa...
This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with reg...
We propose an estimation method that circumvents the path dependence problem existing in Change-Poin...
Identification of switched systems has received considerable attention during the past few years. Th...
Business cycle models are often investigated by using reduced form time series models, other than (o...
We consider state-space representation of a multivariate dynamic process with Markov switching in bo...
Regime Switching models, especially Markov switching models, are regarded as a promising way to capt...