Estimating threshold subset autoregressive moving-average models by genetic algorithms

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.