On a mixture autoregressive model

By C. Wong, Wai Keung Li. 2000

329 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6c7a1ad5832a15adaa8f7f9756356413043a1f3b

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Citations in the corpus

The ACR Model: A Multivariate Dynamic Mixture Autoregression (2008, unknown): “The recent mixture autoregressive model (MAR) by Wong and Li (2000) and its extensions in Wong and Li (2001a, b) and Fong, Li and Wong (2007), as well as the dynamic switching Markov chain model of Gouriéroux and Robert (2006) actually share some features similar to our proposed ACR model.”
Extending the Akaike Information Criterion to Mixture Regression Models (2007, methodology): “Finally, we generalize the MRC’s applicability to mixture quasi-likelihood models (McCullagh and Nelder 1989) and mixture autoregressive time series models (Le, Martin, and Raftery 1996; Wong and Li 2000).”
On a mixture vector autoregressive model (2007, unknown): “All the proofs of the theorems follow those in Le, Martin & Raftery (1996) and Wong & Li (2000) which are based on the results given in Beneš (1967).”
A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors (2001, background): “In the spirit of threshold nonlinear models Wong and Li (2000), Wong and Li (2001a, b) considered mixtures of autoregressive models and mixtures of autoregressive models with ARCH.”
On a Mixture Autoregressive Conditional Heteroscedastic Model (2001, methodology): “The properties of the MAR-ARCH model are similar to those of the MAR model of Wong and Li (2000).”
Threshold models in time series analysis—Some reflections (2015, background): “Wong and Li (2002) adopted the latter formulation and proposed a mixture autoregressive (MAR) model.”
Longitudinal Research Using Mixture Models (2010, methodology): “Variants of this model for continuous response variables ‐ referred to as mixture dynamic regression and mixture autoregressive models ‐ were proposed by Kaplan (2005) and Wong and Li (2000) .”
Markov Switching Models in Empirical Finance (2011, background): “where n42 is the number of t-Student degrees of freedom (an estimable parameter), the GARCH intercept depends on St1⁄4 0,1 while a and b depend on St 11⁄4 0,1. Kim (1994) had addressed the problem that the t-th observation in a K-regime model implies K components of the likelihood function by introd”
An artificial neural network (p, d, q) model for timeseries forecasting (2010, background): “It has also been extensively analyzed in the time series literature with a focus on the nonlinear modeling (Campbell & Walker, 1977; Cornillon, Imam, & Matzner, 2008; Lin & Pourahmadi, 1998; Tang & Ghosal, 2007) see Wong and Li (2000) for a survey.”