Is TRAMO-SEATS automatic identification of Reg-ARIMA ... - Cemfi

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Models:A. AIRLINE-TYPE MODELS (8500 series)Orders: (0 1 1) (0 1 1)(0 1 0) (0 1 1)(0 1 1) (0 1 0)θ θ -0.9 -0.7-0.8 -0.4-0.7 -0.3-0.6 -0.4-0.6 0-0.5 -0.95-0.5 -0.5-0.4 -0.6-0.4 0-0.3 -0.70 -0.70 -0.50.3 -0.60.3 00.4 -0.80.5 -0.611
B. NON-SEASONAL MODELS (8000 series)a. Stationary <strong>Reg</strong>ular ordersx a (0 0 0)1 0.7 (1 0 0) 1 0.6 (0 0 2)1 0.8 1 0.5 (1 0 1)1 0.6 (2 0 0)complex root: 0.78, 7.231 0.41 0.37 1 0.30 0.4 (2 0 1)real roots: 0.85, 0.441 0.3 0.5 . (3 0 0)real root: 0.67complex root: 0.86, 3.1912
Page 1: April 2012Is TRAMO-SEATS automatici
Page 5 and 6: (2) Components in STSM approach: al
Page 8 and 9: REG-ARIMA model:
Page 10 and 11: AUTOMATIC MODEL IDENTIFICATION (TRA
Page 14 and 15: . Non-stationary Orders 1 0.7
Page 16 and 17: 1 0.3 B x 1 0.6 B 1 0.3 B
Page 18 and 19: 16% are stationary (40% of them are
Page 20 and 21: Table 2:Errors in the detection-of-
Page 22 and 23: Next:AODCTable 3:Average number of
Page 24 and 25: Results: For the 25000 series with
Page 26 and 27: Errors in orders of differencing Gr
Page 28 and 29: All tests are carried at the 1% (ap
Page 30 and 31: Table 10:Correct Identification of
Page 32 and 33: But what about REAL SERIES?Generati
Page 34 and 35: Next:Results from TSW+ purely autom
Page 36 and 37: Table 14: % Reduction in residual S
Page 38 and 39: SEATS will reduce it further (throu
Page 40 and 41: Table 17: ARMA parametersGroup (by
Page 42 and 43: Table 19:Real series: Model-diagnos
Page 44 and 45: :- For all groups 99%. and con
Page 46 and 47: OutliersResidual Autocorrelation116
Page 48 and 49: OutliersResidual Autocorrelation%38
Page 50 and 51: %2625242322212019181716151413121110
Page 52 and 53: SEASONALITY AND SEASONAL ADJUSTMENT
Page 54 and 55: SEATS decomposes the series into co
Page 56 and 57: Computational failure: 0%.Misspecif

Is TRAMO-SEATS automatic identification of Reg-ARIMA ... - Cemfi

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