REGIME-SWITCHING MODELS: THRESHOLDS, SMOOTH TRANSITIONS, AND NEURAL NETWORKS / MODELOS COM MÚLTIPLOS REGIMES PARA SÉRIES TEMPORAIS: LIMIARES, TRANSIÇÕES SUAVES E REDES NEURAIS

AUTOR(ES)
DATA DE PUBLICAÇÃO

2000

RESUMO

The goal of this thesis is to propose more flexible regime-switching models combining the ideas from the SETAR, STAR, and ANN specifications. The models discussed in this thesis are models with multi-regimes and with the transition between regimes controlled by a linear combination of known variables. A modelling cycle procedure, based on the work of Teräsvirta and Lin (1993), Eitrheim and Teräsvirta (1996), and Rech, Teräsvirta and Tschernig (1999), consisting of the stages of model specification, parameter estimation, and model evaluation, is developed allowing the practitioner to choose among different alternatives during the modelling cycle. Monte-Carlo simulations and real applications are used to evaluate the performance of the techniques developed here and they suggested that the theory is useful and the proposed models thus seems to be an effective tool for the practicing time series analysts.

ASSUNTO(S)

time series modelos nao-lineares nonlinear models threshold smooth transitions limiares neural networks redes neurais series temporais transicoes suaves

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