Abstract While there has been a great deal of interest in the modelling of non-linearities and regime shifts in economic time series, there is no clear consensus regarding the forecasting abilities of these models. In this paper we develop a general approach to predict multiple time series subject to Markovian shifts in the regime. The feasibility of the proposed forecasting techniques in empir...

This paper tries to analyze effects of trade and financial liberalizations on the Iran’s government size during both long-run and short- run. Accordingly, a specification of the auto regression with distributed lag (ARDL) has been used for investigating the long run relationships between variables, and a vector correction model (VECM) has examined dynamically the short-run relationships betwe...

A Markov switching position dependent random map is a random map of a finite number of measurable transformations where the probability of switching from one transformation to another is controlled by a position dependent irreducible stochastic matrix W . Existence of absolutely continuous invariant measures (acim) for a Markov switching position dependent random map was proved in [1] using spe...

We propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigat...

Abstract: In this paper a new algorithm to identify Auto-Regressive Exogenous Models (ARX) based on Twin Support Vector Machine Regression (TSVR) has been developed. The model is determined by minimizing two ε insensitive loss functions. One of them determines the ε1-insensitive down bound regressor while the other determines the ε2-insensitive up-bound regressor. The algorithm is compared to S...

Hidden Markov models (HMM) are successfully applied in various elds of time series analysis. Colored noise, e.g. due to ltering, violates basic assumptions of the model. While it is well-known how to consider auto-regressive (AR) ltering, there is no algorithm to take into account moving-average (MA) ltering in parameter estimation exactly. We present an approximate likelihood estimator for MA-...

A trivariate vector autoregression time series process, based on a present-value land price model, is used to decompose Iowa farmland prices into fundamental and non-fundamental components. A recent study, by Falk and Lee (1998), found that non-fundamental shocks are an important source of volatility in farmland prices and it was interpreted that these price movements were due to fads not specu...