TY - JOUR
T1 - A flexible coefficient smooth transition time series model
AU - Medeiros, Marcelo C.
AU - Veiga, Álvaro
N1 - Funding Information:
Manuscript received December 9, 2002; revised April 15, 2003. This work was supported by CNPq. This work is partly based on the doctoral dissertation of M. C. Medeiros. M. C. Medeiros is with the Department of Economics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ 22451-900 Brazil. Á. Veiga is with the Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ 22451-900 Brazil. Digital Object Identifier 10.1109/TNN.2004.836246
PY - 2005/1
Y1 - 2005/1
N2 - In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the self-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed.
AB - In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the self-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed.
KW - Neural networks
KW - Smooth transition models
KW - Threshold models
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=13844275978&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=13844275978&partnerID=8YFLogxK
U2 - 10.1109/TNN.2004.836246
DO - 10.1109/TNN.2004.836246
M3 - Article
C2 - 15732392
AN - SCOPUS:13844275978
SN - 1045-9227
VL - 16
SP - 97
EP - 113
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 1
ER -