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

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Next:Results from TSW+ purely automatic use (with parsimonious specificationfor TD pre-testing).I.e., 4, default option.Table 12: GeneralGroup(by# series inAverage% logs Average #Average #% withNZ)grouplengthparameters/seroutliers/sercalendareffect60 – 110 3479 92 82.5 2.1 0.8 23.2111 – 160 3885 126 91.1 2.2 1.3 70.8161 – 210 2945 173 91.3 2.3 1.6 72.6211 – 260 1817 229 80.7 2.2 2.5 28.7261 – 360 1038 290 84.8 2.4 2.8 60.7TOTAL 13164 155 87.0 2.2 1.5 52.0361 – 600 211 504 74.9 2.9 6.4 42.2 More levels in short and long series. Average # of parameters not much affected. Outliers 1/100 observations (all groups). Calendar effect:Groups 1, 4 mostly unaffected;Possibly due to group composition (e.g., FTI prone to exhibitcalendar effect.33
Table 12: OutliersGroup (by NZ)% of serieswith out.Average # per seriesAO TC LS Tot.60 – 110 42.2 0.4 0.2 0.2 0.8111 – 160 59.5 0.6 0.3 0.4 1.3161 – 210 67.1 0.8 0.4 0.5 1.6211 – 260 82.3 1.1 0.5 0.9 2.5261 – 360 85.0 1.6 0.6 0.6 2.8TOTAL AVERAGE 61.8 0.7 0.3 0.4 1.5361 – 600 93.0 2.5 1.4 2.0 5.9 % of series with outlier increases with length. 1 outlier / 100 observations. # of AO (# of TC + # of LS).Table 13: Calendar effectsGroup% of series withTD EE STOCH. TD Calendar effect60 – 110 19.5 2.6 2.3 23.2111 – 160 68.7 17.5 3.1 70.8161 – 210 69.3 25.4 5.0 72.6211 – 260 23.9 11.9 3.1 28.7261 – 360 56.2 27.6 2.6 60.7TOTAL AVERAGE 48.7 15.4 3.3 52.0361 – 600 37.4 13.1 8.3 42.134
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 12 and 13: Models:A. AIRLINE-TYPE MODELS (8500
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 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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