In this paper, a general autoregressive model with Markov switching is considered, where the autoregression may be of an infinite order. The consistency of the maximum likelihood estimators for this model is obtained under regular assumptions. Examples of finite and infinite order Markov switching AR models are discussed. The simulation study with these examples illustrates the consistency and ...

T he objective of this study is using the Markov Switching Vector Autoregressive method and regime dependent impulse response functions to measure the pass-through of world food prices to consumer price index in Iran from 1990 to 2013. With respect to information criteria and the log-likelihood ratio statistic, MSIA(2)-VAR(1) model has a better fit to data than other models. The magn...

This paper proposes a discrete-state stochastic volatility model with duration-dependent mixing. The latter is directed by a high-order Markov chain with a sparse transition matrix. Ž . As in the standard first-order Markov switching MS model, this structure can capture turning points and shifts in volatility, due for example, to policy changes or news events. Ž . However, the duration-dependen...

We examine a Markov Switching model of Singaporean GDP using a combination of formal moment-based tests and informal graphical tests. The tests confirm that the Markov Switching model fits the data better than a linear, autoregressive alternative. The methods are extended to allow us to identify precisely which features of the data are better captured by the non-linear model. The methods descri...

this paper examines regime shifts in tedpix return and volatility and the effects of positive and negative crude oil shocks and gold price fluctuations on stock market shifts behavior using markov switching egarch model with student’s t-distribution. we detect two episodes of series behavior, one relative to low mean/high variance regime namely bear state and the other to high mean/low variance...

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...

This paper presents a new nonlinear time series model that captures a postrecession “bounce-back” in the level of aggregate output. While a number of studies have examined this type of business cycle asymmetry using recession-based dummy variables and threshold models, we relate the “bounce-back” effect to an endogenously estimated unobservable Markov-switching state variable. When the model is...

Real economic data always present nonlinear properties such as asymmetry and radically change in the series through time. Missing data and jumps as well as breaks also common reported in economic time series model. Thus, linear models are no longer suitable used in estimate the economic data and markov switching vector autoregressive model (MS-VAR) is applied in study the economic model. This p...

The objective of this paper is to provide readers with the program to estimate a Markov switching model with time varying transition probability(Filardo, 1994) by using a statistical computing software R. Although many of the previous studies estimating the model have conducted the estimation by the maximum likelihood estimation, this paper utilizes Gibbs sampling method. Using Gibbs sampling m...