Add to ORKGIn this paper we consider model selection for a Markov switching vector error correction model. We apply the algorithm proposed by Chib (1995) to calculate the marginal likelihood and the Bayes factors for this model to select the most appropriate model among all models under consideration. We perform a simple Monte Carlo simulation to illustrate the performances of the method for model selection
marginal likelihood estimation In ML model selection we judge models by their ML score and the numbe...
We review the across-model simulation approach to computation for Bayesian model determination, base...
RePEc Working Paper Series: No. 03/2011This review surveys a number of common Model Selection Algori...
In this paper we consider model selection for a Markov switching vector error correction model. We a...
This paper introduces a Bayesian approach to a Markov switching vector error correction model that a...
We study model selection issues and some extensions of Markov switching models. We establish both th...
This paper introduces statistical inference in a Markov switching vector error correction model usin...
In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and ...
This paper is concerned with the problem of joint determination of the state dimension and autoregre...
In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and ...
In the present paper we explore various approaches of computing model likelihoods from the MCMC outp...
This paper proposes a model selection approach for the specification of the cointegrating rank in th...
We develop methods for Bayesian inference in vector error correction models which are subject to a v...
Jump Markov linear models consists of a finite number of linear state space models and a discrete va...
We develop methods for Bayesian inference in vector error correction models which are subject to a v...
marginal likelihood estimation In ML model selection we judge models by their ML score and the numbe...
We review the across-model simulation approach to computation for Bayesian model determination, base...
RePEc Working Paper Series: No. 03/2011This review surveys a number of common Model Selection Algori...
In this paper we consider model selection for a Markov switching vector error correction model. We a...
This paper introduces a Bayesian approach to a Markov switching vector error correction model that a...
We study model selection issues and some extensions of Markov switching models. We establish both th...
This paper introduces statistical inference in a Markov switching vector error correction model usin...
In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and ...
This paper is concerned with the problem of joint determination of the state dimension and autoregre...
In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and ...
In the present paper we explore various approaches of computing model likelihoods from the MCMC outp...
This paper proposes a model selection approach for the specification of the cointegrating rank in th...
We develop methods for Bayesian inference in vector error correction models which are subject to a v...
Jump Markov linear models consists of a finite number of linear state space models and a discrete va...
We develop methods for Bayesian inference in vector error correction models which are subject to a v...
marginal likelihood estimation In ML model selection we judge models by their ML score and the numbe...
We review the across-model simulation approach to computation for Bayesian model determination, base...
RePEc Working Paper Series: No. 03/2011This review surveys a number of common Model Selection Algori...