On Joint Determination of the Number of States and the Number of Variables in Markov-Switching Models: A Monte Carlo Study (original) (raw)
Related papers
Markov-switching model selection using Kullback–Leibler divergence
Journal of Econometrics, 2006
In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. In applying Akaike information criterion (AIC), which is an estimate of KL divergence, we find that AIC retains too many states and variables in the model. Hence, we derive a new information criterion, Markov switching criterion (MSC), which yields a marked improvement in state determination and variable selection because it imposes an approriate penalty to mitigate the over-retention of states in the Markov chain. MSC performs well in Monte Carlo studies with single and multiple states, small and large samples, and low and high noise. Furthermore, it not only applies to Markov-switching regression models, but also performs well in Markov-switching autoregression models. Finally, the usefulness of MSC is illustrated via applications to the U.S. business cycle and the effectiveness of media advertising.
the Multi-State Markov Switching Model
Econometrics, 2003
In many real phenomena the behaviour of a certain variable, subjected to different regimes, depends on the state of other variables or the same variable observed in other subjects, so the knowledge of the state of the latter could be important to forecast the state of the former. In this paper a particular multivariate Markov Switching model is developed to represent this case. The transition probabilities of this model are characterized by the dependence on the regime of the other variables. The estimation of the transition probabilities provides useful informations for the researcher to forecast the regime of the variables analyzed. Theoretical background and an application are shown.
The multi-chain Markov switching model
Journal of Forecasting, 2005
In many real phenomena the behaviour of a certain variable, subject to different regimes, depends on the state of other variables or the same variable observed in other subjects, so the knowledge of the state of the latter could be important to forecast the state of the former. In this paper a particular multivariate Markov switching model is developed to represent this case. The transition probabilities of this model are characterized by the dependence on the regime of the other variables. The estimation of the transition probabilities provides useful information for the researcher to forecast the regime of the variables analysed. Theoretical background and an application are shown.
State Space Markov Switching Models Using
2008
We propose a state space model with Markov switching, whose regimes are associated with the model parameters and regime transition probabilities are time-dependent. The estimation is based on maximum likelihood method using the EM algorithm. The distribution of the estimators is assessed using bootstrap. To evaluate the state variables and regime probabilities, the Kalman filter and a probability filter procedure conditional to each possible regime at each instant are used. This procedure is illustrated with simulated data and the United States monthly industrial production index from January 1960 to January 1995.
In this paper we develop a test procedure to test for a dimension reduction in Markov chains. We try to test whether multiple series follow the same underlying Markov chain. This might help to explain the relation between the Markov-switching component of the model and the different observed variables, think for example about the effect of an economic crisis on different countries. The test procedure we propose combines the ideas of Engle & Kozicki (1993) with Hansen's (1992) likelihood ratio test. The promise shown by this new procedure is illustrated with a Monte Carlo experiment and an empirical application.
Advances in Markov-Switching Models
2002
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
MS_Regress-The MATLAB Package for Markov Regime Switching Models
2012
Markov state switching models are a type of specification which allows for the transition of states as an intrinsic property of the econometric model. Such type of statistical representations are well known and utilized in different problems in the field of economics and finance. This paper gives an overview of MS Regress, a MATLAB toolbox specially designed for the estimation, simulation and forecasting of a general markov regime switching model. The package was written in an intuitive manner so that the user have at its reach a large number of different markov switching specifications, without any change in the original code. This document introduces the main functionality of the package with the help of several empirical examples.
A nonparametric bayesian approach to detect the number of regimes in markov switching models
2002
for monetary effects on output). These models share the characteristics of assuming known the number of regimes (states), that is, the number of generating data processes which differ from one another just for the value of the parameters. Working with a known number of regimes avoids the problem of selecting that number, since it would require hypothesis testing with nuisance parameters identified only under the alternative.[5] 5. Andrews, DWK and Ploberger, W. 1994.
Consistent estimation of the number of regimes in Markov-switching autoregressive models
Communications in Statistics - Theory and Methods, 2020
Markov-switching models have become a popular tool in areas ranging from finance to electrical engineering. Determining the number of hidden regimes in such models is a key problem in applications. This paper proposes a strongly consistent estimator of the number of regimes for Markov-switching autoregressive models. By using subadditive ergodic theorem, law of iterated logarithm for martingales, together with results from information theory, we derive sufficient conditions to avoid underestimation as well as overestimation. In particular, we propose a modified information criterion, regimeswitching information criterion (RSIC) which generates a simple and consistent model selection procedure. Finally, we conduct a Monte Carlo study to evaluate the efficacy of our procedure in finite sample and also compare the performance of RSIC with popular information criteria including AIC, BIC and HQC.
Through the applied literature, the Markov switching with time varying transition probabilities (MS-TVTP) is considered as one of the most relevant models. The aim of this paper is to shed light users of MS-VAR model in the analysis of causal relationships in macroeconomic time series. Through a revision of the Filardo and Gordon’s algorithm and through the adaptation Gibbs sampling, dealing with Bayesian probability, we provided a detailed Matlab code that estimates the MS-TVTP parameters. In our application, we have shown (i) the possibility and relevance of our extension in the analysis of cyclical fluctuations of the bilateral exchange rate TND/USD, and the index of industrial production, (ii) the dependency of the current transition probability of the exchange rate on that of the previous period, of the industrial production, and of economic growth, (iii) the persistence probability of the Tunisian Dinar in the phase of depreciation outweighs that of persistence in the appreciation phase. This is due to the opening of the Tunisian industrial sector to a more competitive industry in Europe, (iv) the necessity to implement this extension for more general applications on cyclical fluctuations in the small open economies.