Smooth Transitions, Neural Networks, and Linear Models - CiteSeerX

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Table 5: Estimation results - ANN model.Series Lags Hidden Units ÑÒ´¦µ Austria 6 5 0.9680 0.0264Belgium 6 5 0.9098 0.0256Finl<strong>and</strong> 6 5 0.9603 0.0224France 6 5 0.9997 0.0254Germany 6 5 0.9603 0.0266Sweden 6 5 0.9835 0.0223Australia 6 5 0.2395 0.0213Spain 6 5 0.8734 0.0248Siri Lanka 10 12 0.0156 0.0051India 10 12 1.0000 0.017730
Table 6: Number of series where each model is the best model.1-step-ahead2-step-aheadNCSTAR ANN AR RW NCSTAR ANN AR RWnRMSE 1 2 5 2 2 1 3 4MAE 2 2 5 1 2 0 5 3MAD 2 1 5 2 3 5 1 1SIGN 3 2 3 4 3 3 4 63-step-ahead4-step-aheadNCSTAR ANN AR RW NCSTAR ANN AR RWnRMSE 2 2 4 2 4 2 3 1MAE 3 1 2 4 2 1 3 4MAD 6 2 0 2 4 3 1 2SIGN 3 4 2 5 1 4 3 631
Page 1 and 2: Modelling Exchange Rates:Smooth Tra
Page 3 and 4: alternatives. The results are discu
Page 5 and 6: (b) Test linearity.(c) If linearity
Page 7 and 8: the standard asymptotic distributio
Page 9 and 10: 2.2.3 Model EvaluationAfter the NCS
Page 11 and 12: where Ô Ø is the price at the tim
Page 13: significant in a few number of peri
Page 16 and 17: ased on Bayesian fundamentals. Conc
Page 18 and 19: Nguyen, D. and Widrow, B. (1990). I
Page 20 and 21: Consider the simple nonlinear model
Page 22 and 23: In this paper we adopt the followin
Page 24 and 25: 20 40 60 80 100 120 140 160 180Aust
Page 26 and 27: Table 1: Data sets.Series Descripti
Page 28 and 29: Table 3: Specification and estimati
Page 32 and 33: Table 7: Number of series where mod

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