Diagnostic Checking in a Flexible Nonlinear Time Series Model

Medeiros, Marcelo C.; Veiga, Álvaro

doi:10.1111/1467-9892.00316

Cited by 21 publications

(26 citation statements)

References 21 publications

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“…However, a sequence of neglected nonlinearity tests can also be interpreted as model evaluation tests. The construction of tests for serial correlation, in the spirit of Eitrheim and Teräsvirta (1996) and Medeiros and Veiga (2003), is also possible.…”

Section: Model Selectionmentioning

confidence: 99%

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

McAleer

Medeiros

2008

Journal of Econometrics

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144

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a b s t r a c tIn this paper we propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogeneous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to simultaneously approximate long memory behavior, as well as describe sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.

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Section: Model Selectionmentioning

confidence: 99%

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

McAleer

Medeiros

2008

Journal of Econometrics

Self Cite

144

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“…This formulation allows the variance to change smoothly between regimes. Following [21], to avoid complicated restrictions over the parameters to guarantee a positive variance, we rewrite (8) as…”

Section: Test Of Homoscedasticity Of the Residuals Of An Frbmmentioning

confidence: 99%

A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters

et al. 2011

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Heteroscedasticy is the property of having a changing variance throughout the time. Homoscedasticity is the converse, that is, having a constant variance. This is a key property for time series models which may have serious consequences when making inferences out of the errors of a given forecaster. Thus it has to be conveniently assessed in order to establish the quality of the model and its forecasts. This is important for every model including fuzzy rule-based systems, which have been applied to time series analysis for many years. Lagrange multiplier testing framework is used to evaluate wether the residuals of an FRBS are homoscedastic. The test robustness is thoroughly evaluated through an extensive experimentation. This is another important step towards a statistically sound modeling strategy for fuzzy rule-based systems.

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“…This formulation allows the variance to change smoothly between regimes. Following [8], in order to avoid complicated restrictions over the parameters to guarantee a positive variance, we rewrite equation (8) as…”

Section: Test Of Homoscedasticity Of the Residuals Of An Frbmmentioning

confidence: 99%

Testing for Heteroskedasticity of the Residuals in Fuzzy Rule-Based Models

Aznarte

Benítez

2010

Trends in Applied Intelligent Systems

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To cite this version:José-Luis Aznarte, José-M. Benitez. Testing for heteroskedasticity of the residuals in fuzzy rule-based models. García-Pedrajas, N.; Herrera, Abstract. In this paper, we propose a new diagnostic checking tool for fuzzy rule-based modelling of time series. Through the study of the residuals in the Lagrange Multiplier testing framework we devise a hypothesis test which allows us to determine if the residual time series is homoscedastic or not, that is, if it has the same variance throughout time. This is another important step towards a statistically sound modelling strategy for fuzzy rule-based models.

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Diagnostic Checking in a Flexible Nonlinear Time Series Model

Cited by 21 publications

References 21 publications

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters

Testing for Heteroskedasticity of the Residuals in Fuzzy Rule-Based Models

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