On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach

Chen, Cathy W. S.; Cherng, Tsai-Hung; Wu, Berlin

doi:10.1007/s180-001-8327-9

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…For identifying and estimating SETAR models GAs were proposed by Wu and Chang [12] for the two-regimes case. Also applications of GAs in threshold model framework were presented by Baragona et al [13] for subset SETAR model identification in the presence of a moving average (MA) part and by Chen et al [14] for bilinear subset model selection.…”

Section: Introductionmentioning

confidence: 99%

Double threshold autoregressive conditionally heteroscedastic model building by genetic algorithms

Baragona¹,

Cucina²

2008

Journal of Statistical Computation and Simulation

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Asymmetric behaviour in both mean and variance is often observed in real time series. The approach we adopt is based on double threshold autoregressive conditionally heteroscedastic (DTARCH) model with normal innovations. This model allows threshold nonlinearity in mean and volatility to be modelled as a result of the impact of lagged changes in assets and squared shocks, respectively. A methodology for building DTARCH models is proposed based on genetic algorithms (GAs). The most important structural parameters, that is regimes and thresholds, are searched for by GAs, while the remaining structural parameters, that is the delay parameters and models orders, vary in some pre-specified intervals and are determined using exhaustive search and an Asymptotic Information Criterion (AIC) like criterion. For each structural parameters trial set, a DTARCH model is fitted that maximizes the (penalized) likelihood (AIC criterion). For this purpose the iteratively weighted least squares algorithm is used. Then the best model according to the AIC criterion is chosen. Extension to the double threshold generalized ARCH (DTGARCH) model is also considered. The proposed methodology is checked using both simulated and market index data. Our findings show that our GAs-based procedure yields results that comparable to that reported in the literature and concerned with real time series. As far as artificial time series are considered, the proposed procedure seems to be able to fit the data quite well. In particular, a comparison is performed between the present procedure and the method proposed by Tsay [Tsay, R.S., 1989, Testing and modeling threshold autoregressive processes. Journal of the American Statistical Association, Theory and Methods, 84, 231-240.] for estimating the delay parameter. The former almost always yields better results than the latter. However, adopting Tsay's procedure as a preliminary stage for finding the appropriate delay parameter may save computational time specially if the delay parameter may vary in a large interval

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Section: Introductionmentioning

confidence: 99%

Double threshold autoregressive conditionally heteroscedastic model building by genetic algorithms

Baragona¹,

Cucina²

2008

Journal of Statistical Computation and Simulation

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“…33 Zhang et al synthesized a Sr 3 Al 2 O 6 :Eu 2+ phosphor using the sol-gel method and found that the calcination temperature has a major role in the morphology, crystalline structure, and luminescence output. and ethylene glycol are used as the solvent and polymerizing agent, respectively.…”

Section: Sol-gel Methodsmentioning

confidence: 99%

Developments in Eu⁺²‐Doped Strontium Aluminate and Polymer/Strontium Aluminate Composite

et al. 2014

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The discovery of rare earth based phosphors in 1960s started a new era of luminescence. In comparison with sulfide-based phosphors, strontium aluminates received worldwide acceptance because of their chemical stability and good luminescent properties. The SrAl x O y :Eu 2+ ,Dy 3+ phosphors excited by UV or visible light emit phosphorescence in the blue-green region after the excitation source has been removed, and the phosphorescence last for more than 15 h. The major disadvantage of SrAl x O y :Eu 2+ ,Dy 3+ phosphors is that they are prone to hydrolysis and their luminescent properties gradually deteriorate. The dispersion of these phosphors in a waterproof polymer matrix is an alternative method reported for retaining their luminescent properties. The review extensively deals with strontium aluminate based luminescent materials, the mechanism of luminescence, their synthesis techniques, and different mixing procedures to prepare polymer/strontium aluminate composites. Different methods to improve the dispersion of the strontium aluminate in the polymer matrix are also outlined. C

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“…The use of genetic algorithms was suggested specifically for building threshold models (Wu and Chang, 2002) and other nonlinear dynamics (e.g. for bilinear models, see Chen et al. 2001).…”

Section: Multi‐regime Modelsmentioning

confidence: 99%

Time-varying multi-regime models fitting by genetic algorithms

Battaglia

Protopapas

2010

Journal of Time Series Analysis

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Many time series exhibit both nonlinearity and non-stationarity. Though both features have been often taken into account separately, few attempts have been proposed for modelling them simultaneously. We consider threshold models, and present a general model allowing for different regimes both in time and in levels, where regime transitions may happen according to self-exciting, or smoothly varying or piecewise linear threshold modelling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The performance of the proposed procedure is illustrated with a simulation study and applications to some real data

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On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach

Cited by 9 publications

References 10 publications

Double threshold autoregressive conditionally heteroscedastic model building by genetic algorithms

Double threshold autoregressive conditionally heteroscedastic model building by genetic algorithms

Developments in Eu⁺²‐Doped Strontium Aluminate and Polymer/Strontium Aluminate Composite

Time-varying multi-regime models fitting by genetic algorithms

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On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach

Cited by 9 publications

References 10 publications

Double threshold autoregressive conditionally heteroscedastic model building by genetic algorithms

Double threshold autoregressive conditionally heteroscedastic model building by genetic algorithms

Developments in Eu+2‐Doped Strontium Aluminate and Polymer/Strontium Aluminate Composite

Time-varying multi-regime models fitting by genetic algorithms

Contact Info

Product

Resources

About

Developments in Eu⁺²‐Doped Strontium Aluminate and Polymer/Strontium Aluminate Composite