A genetic algorithm is proposed to estimate the parameters of a selfexciting threshold subset autoregressive moving-average model. The threshold model is composed of several linear autoregressive moving-average models. Each one of these models applies according to a "switch mechanism" that is based on the comparison between the delayed observation and some "threshold" values. Our procedure incorporates the identification in each "regime" of a "subset" model. Subset models are useful as they allow the number of parameters to be reduced so that only those really needed are included in the model. The proposed procedure is used for modeling the well-known Canadian lynx data
Nonlinear nonstationary models for time series are considered, where the series is generated from an...
Dipartimento di Statistica, Probabilita' e Statistiche Applicate, Working Paper 2009 - n. 10, URL...
In this paper we fit non-linear models. We build Threshold Autoregressive (TAR) and Generalized Auto...
A genetic algorithm is proposed to estimate the parameters of a selfexciting threshold subset autore...
ABSTRACT: The class of the subset threshold autoregressive moving average is introduced. A genetic a...
A nonlinear version of the threshold autoregressive model for time series is introduced. A peculiar ...
Several nonlinear time series models have been proposed in the literature to explain various empiric...
Many time series exhibit both nonlinearity and non-stationarity. Though both features have been ofte...
<div><p>The threshold autoregressive (TAR) model is a class of nonlinear time series models that hav...
Many time series exhibits both nonlinearity and nonstationarity. Though both features have been ofte...
近幾年來,非線性時間數列分析有快速的發展。其中的門檻自迴歸模式(SETAR),以具有許多線性ARIMA模式所不能配適的特性而受到重視。但是,自1978年Tong建立SETAR模式以來,門檻參數估計...
ABSTRACT. Over recent years, several nonlinear time series models have been proposed in the literatu...
Abstract: Selecting the threshold variable is a key step in building a generalized threshold autoreg...
Asymmetric behaviour in both mean and variance is often observed in real time series. The approach w...
In this paper we propose a new class of nonlinear time series models, the threshold variable driven ...
Nonlinear nonstationary models for time series are considered, where the series is generated from an...
Dipartimento di Statistica, Probabilita' e Statistiche Applicate, Working Paper 2009 - n. 10, URL...
In this paper we fit non-linear models. We build Threshold Autoregressive (TAR) and Generalized Auto...
A genetic algorithm is proposed to estimate the parameters of a selfexciting threshold subset autore...
ABSTRACT: The class of the subset threshold autoregressive moving average is introduced. A genetic a...
A nonlinear version of the threshold autoregressive model for time series is introduced. A peculiar ...
Several nonlinear time series models have been proposed in the literature to explain various empiric...
Many time series exhibit both nonlinearity and non-stationarity. Though both features have been ofte...
<div><p>The threshold autoregressive (TAR) model is a class of nonlinear time series models that hav...
Many time series exhibits both nonlinearity and nonstationarity. Though both features have been ofte...
近幾年來,非線性時間數列分析有快速的發展。其中的門檻自迴歸模式(SETAR),以具有許多線性ARIMA模式所不能配適的特性而受到重視。但是,自1978年Tong建立SETAR模式以來,門檻參數估計...
ABSTRACT. Over recent years, several nonlinear time series models have been proposed in the literatu...
Abstract: Selecting the threshold variable is a key step in building a generalized threshold autoreg...
Asymmetric behaviour in both mean and variance is often observed in real time series. The approach w...
In this paper we propose a new class of nonlinear time series models, the threshold variable driven ...
Nonlinear nonstationary models for time series are considered, where the series is generated from an...
Dipartimento di Statistica, Probabilita' e Statistiche Applicate, Working Paper 2009 - n. 10, URL...
In this paper we fit non-linear models. We build Threshold Autoregressive (TAR) and Generalized Auto...