Estimation of Linear Autoregressive Models with Markov-switching, the E.m. Algorithm Revisited
نویسنده
چکیده
This work concerns estimation of linear autoregressive models with Markov-switching using expectation maximisation (E.M.) algorithm. Our method generalise the method introduced by Elliot for general hidden Markov models and avoid to use backward recursion.
منابع مشابه
Estimating Stock Price in Energy Market Including Oil, Gas, and Coal: The Comparison of Linear and Non-Linear Two-State Markov Regime Switching Models
A common method to study the dynamic behavior of macroeconomic variables is using linear time series models; however, they are unable to explain nonlinear behavior of the series. Given the dependency between stock market and derivatives, the behavior of the underlying asset price can be modeled using Markov switching process properties and the economic regime significance. In this paper, a two-...
متن کاملFads Models with Markov Switching Hetroskedasticity: decomposing Tehran Stock Exchange return into Permanent and Transitory Components
Stochastic behavior of stock returns is very important for investors and policy makers in the stock market. In this paper, the stochastic behavior of the return index of Tehran Stock Exchange (TEDPIX) is examined using unobserved component Markov switching model (UC-MS) for the 3/27/2010 until 8/3/2015 period. In this model, stock returns are decomposed into two components; a permanent componen...
متن کاملNon-Linear Relationships Among Oil Price, Gold Price and Stock Market Returns in Iran: A Multivariate Regime-Switching Approach
In this paper, the effects of oil and gold prices on stock market index are investigated. We use a cointegrated vector autoregressive Markov-switching model to examine the nonlinear properties of these three variables during the period of January 2003 - December 2014. The Markov-switching vector-equilibrium-correction model with three regimes representing "deep recession", "mild recession" and ...
متن کاملModeling Gasoline Consumption Behaviors in Iran Based on Long Memory and Regime Change
In this study, for the first time, we model gasoline consumption behavior in Iran using the long-term memory model of the autoregressive fractionally integrated moving average and non-linear Markov-Switching regime change model. Initially, the long-term memory feature of the ARFIMA model is investigated using the data from 1927 to 2017. The results indicate that the time series studied has a lo...
متن کاملSparse vector Markov switching autoregressive models. Application to multivariate time series of temperature
Multivariate time series are of interest in many fields including economics and environment. The dynamical processes occurring in these domains often exhibit regimes so that it is common to describe them using Markov Switching vector autoregressive processes. However the estimation of such models is difficult even when the dimension is not so high because of the number of parameters involved. I...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008