A Markov Switching Autoregressive – MS-AR – approach is proposed herein for wind power forecast errors. This particular model is able to identify weather regimes according to the forecast reliability. Such regimes are controlled by a Markov chain whose state – not directly observable – ...
In this paper, we investigate the properties of alternative procedures that can be used to determine the state dimension of a Markov-switching autoregressive model. These include procedures that exploit the ARMA representation which Markov-switching processes admit, as well as procedures that are based...
We develop a Markov-Switching Autoregressive Conditional Intensity model for high-frequency volatility modelling via the absolute price change point process. By incorporating a regime-switching relationship between price durations and trading volume, we discover two distinct regimes with a dominant regime ...
The business cycle properties are found to be very sensitive to the state dimension, the choice of the MS model (classified according to regime-dependent parameters) and the autoregressive lag order. The chosen two-regime MS model suggests four recessionary and five expansionary phases in the post...
Autoregressive modelCharacteristicMarkov switching modelThe objective of this paper is to explore the issue of whether the numbers of regimes and variables in aggregate time series are similar to those in individual time series. Equal and value weighted......
This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variable... J Lei - 《Ssrn Electronic Journal》 被引量: 9发表: 2013年 A filtered EM algorithm for joint hidden Markov model and sinusoidal parameter...
Statistical tests of the models' specification indicate that the Markov switching model is better able to capture the non-stationary features of the data than the threshold autoregressive model, although both represent superior descriptions of the data than the models that allow for only one state. ...
We contribute to the theoretical understanding of Markov-switching Vector AutoRegressive (MS VAR) processes by making available conditional moments given regimes — i.e. moments conditional on any state or sequence of states — up to the fourth order. These conditional moments have several utilities....
The proposed methodology is based on a Markov-switching autoregressive model with time-varying coefficients. An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in ...
We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus ...