TAR模型的建模過程包括參數估計與模型選取。後者包括選取threshold 變數與AR order。threshold 變數選取的好壞，對後續的其他估計步驟有決定性的影響。由F統計量或MLE法選取threshold 變數均相當簡便，然而本文的模擬結果顯示，在某些情況下，兩者選出的threshold 變數並不一定可以進一步適配出有良好預測力的模型，此時配適多個TAR模型再將其預測取平均，可以有效降低PMSE等預測誤差指標。在實證分析上，以類流感、加拿大山貓以及太陽黑子三筆數據作實例探討。

Parameter estimation and model selection are two main steps in the procedure of modeling threshold autoregressive model. The latter one includes selection of threshold variable and AR order, which play key role in the performance of modeling. Selecting threshold variable according to F statistic proposed by Tsay (1989) or according to method of MLE are two simple and widely used methods. However, it is showed in present study by simulation as well as real data that threshold variables selected by these two methods not necessary brings the fitted model with good prediction performance. Modeling two threshold autoregressive models according to the first two threshold variables chosen by either of the two methods and then averaging the one-step prediction usually decrease PMSE. The ILI, Lynx and sunspot data are used for illustration.

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