Tramo is a particular regarima model estimation algorithm, mainly used to linearized the series before performing a decomposition with Seats
Arguments
- y
the dependent variable (a
tsobject).- order, seasonal
the orders of the ARIMA model.
- mean
Boolean to include or not the mean.
- X
user defined regressors (other than calendar).
- X.td
calendar regressors.
- ao, ls, so, tc
Boolean to indicate which type of outliers should be detected.
- cv
numeric. The entered critical value for the outliers' detection procedure. If equal to 0 the critical value for the outliers' detection procedure is automatically determined by the number of observations.- ml
Use of maximum likelihood (otherwise approximation by means of Hannan-Rissanen).
- clean
Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be.
Examples
tramo_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
#> $model
#> $model$y
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct
#> 1982 460.1 502.6 443.8 459.1 438.4 465.1 452.7
#> 1983 379.2 378.0 472.1 503.4 510.6 462.4 468.3 458.2 482.7 485.3
#> 1984 414.7 414.5 484.7 487.3 597.9 500.4 543.4 503.4 522.8 556.6
#> 1985 516.3 452.5 525.8 587.7 700.3 561.8 602.8 582.5 563.1 637.1
#> 1986 570.5 478.2 547.4 594.3 751.6 553.4 663.2 581.1 661.9 665.6
#> 1987 613.9 513.2 599.9 674.1 714.0 670.5 720.9 601.6 672.3 709.1
#> 1988 631.0 551.1 678.1 715.7 740.8 722.0 683.5 650.9 723.3 729.6
#> 1989 631.5 552.0 719.0 697.6 764.8 786.3 715.1 723.8 757.9 751.7
#> 1990 678.2 586.2 726.8 744.1 815.5 832.4 710.3 759.4 741.1 786.6
#> 1991 694.0 604.7 719.2 748.2 828.2 746.9 794.5 770.4 741.5 858.6
#> 1992 740.0 665.9 701.5 831.4 878.6 826.0 788.2 723.6 819.8 902.5
#> 1993 762.1 643.0 754.1 840.7 906.6 887.1 771.5 728.7 844.7 886.9
#> 1994 745.7 664.4 821.5 831.7 908.0 912.6 782.9 798.8 887.0 934.6
#> 1995 752.4 682.5 811.2 906.0 927.2 906.8 880.6 873.9 856.8 920.6
#> 1996 833.1 737.1 812.0 895.2 962.8 908.6 908.0 888.9 833.7 933.7
#> 1997 840.9 727.4 857.9 849.0 994.8 830.2 971.1 836.0 939.1 976.9
#> 1998 917.3 716.2 822.9 970.1 970.2 849.4 1042.3 869.9 939.4 1021.3
#> 1999 942.0 738.4 903.2 953.2 1011.2 894.4 1054.5 899.5 1002.3 1043.7
#> 2000 924.9 798.2 901.9 1024.7 1052.3 1165.5 859.3 1009.2 1054.6 1070.4
#> 2001 971.9 814.6 1017.5 1039.2 1123.5 1024.9 1100.8 963.0 1012.9 1132.0
#> 2002 1027.9 841.4 1043.9 1075.3 1190.9 1143.0 1075.7 1065.9 1060.1 1211.4
#> 2003 1099.3 900.5 1092.7 1222.4 1237.1 1237.9 1182.0 1101.2 1198.2 1316.1
#> 2004 1182.9 989.8 1131.4 1277.1 1280.3 1384.1 1305.9 1166.8 1317.9 1358.3
#> 2005 1246.3 1037.3 1300.8 1153.7 1264.2 1454.2 1290.1 1210.7 1277.8 1314.4
#> 2006 1193.7 1037.7 1204.5 1348.6 1267.6 1429.0 1412.0 1239.2 1219.1 1344.6
#> 2007 1267.3 1047.0 1331.6 1302.6 1365.1 1491.5 1462.3 1315.5 1353.3 1440.6
#> 2008 1397.8 1140.5 1351.7 1396.6 1421.1 1401.6 1582.3 1268.4 1383.3 1452.4
#> 2009 1451.0 1056.6 1386.9 1509.1 1519.4 1500.5 1570.7 1341.5 1399.9 1534.3
#> 2010 1469.1 1111.9 1379.9 1389.7 1427.2 1551.4 1581.0 1324.0 1422.0 1464.9
#> 2011 1412.6 1117.5 1321.6 1472.6 1408.9 1471.9 1532.5 1293.5 1345.7 1404.7
#> 2012 1362.4 1131.7 1349.2 1391.2 1456.9 1616.4 1423.4 1359.0 1367.8 1442.6
#> 2013 1397.4 1113.6 1397.3 1339.1 1441.9 1537.4 1390.6 1337.2 1359.4 1463.3
#> 2014 1451.0 1064.9 1293.2 1442.9 1411.8 1461.6 1501.6 1254.2 1356.4 1478.7
#> 2015 1471.2 1053.8 1367.2 1442.2 1428.7 1480.9 1540.9 1331.9 1400.1 1566.3
#> 2016 1519.2 1155.8 1451.5 1451.0 1449.7 1596.1 1468.3 1293.9 1393.5 1497.4
#> 2017 1428.5 1092.4 1370.3 1522.6 1452.4 1557.2 1445.5 1303.1
#> Nov Dec
#> 1982 522.9 889.3
#> 1983 568.7 963.7
#> 1984 623.2 1039.4
#> 1985 697.1 1187.5
#> 1986 700.9 1367.9
#> 1987 743.2 1460.1
#> 1988 870.3 1570.0
#> 1989 923.8 1569.4
#> 1990 931.5 1563.1
#> 1991 944.7 1600.3
#> 1992 968.6 1650.9
#> 1993 970.0 1710.5
#> 1994 1000.4 1817.5
#> 1995 1067.4 1857.2
#> 1996 1081.6 1837.6
#> 1997 1111.3 1879.1
#> 1998 1137.7 1975.7
#> 1999 1207.2 2069.6
#> 2000 1232.5 2177.5
#> 2001 1344.8 2269.5
#> 2002 1495.1 2338.6
#> 2003 1528.2 2424.2
#> 2004 1536.7 2500.8
#> 2005 1540.4 2536.0
#> 2006 1623.3 2611.1
#> 2007 1687.9 2747.0
#> 2008 1675.9 2886.1
#> 2009 1736.6 2795.1
#> 2010 1705.5 2752.4
#> 2011 1660.0 2730.5
#> 2012 1672.9 2753.3
#> 2013 1668.9 2725.5
#> 2014 1687.7 2756.9
#> 2015 1730.5 2913.6
#> 2016 1684.3 2850.4
#> 2017
#>
#> $model$variables
#> [1] "AO.220" "AO.219"
#>
#> $model$X
#> [,1] [,2]
#> [1,] 0 0
#> [2,] 0 0
#> [3,] 0 0
#> [4,] 0 0
#> [5,] 0 0
#> [6,] 0 0
#> [7,] 0 0
#> [8,] 0 0
#> [9,] 0 0
#> [10,] 0 0
#> [11,] 0 0
#> [12,] 0 0
#> [13,] 0 0
#> [14,] 0 0
#> [15,] 0 0
#> [16,] 0 0
#> [17,] 0 0
#> [18,] 0 0
#> [19,] 0 0
#> [20,] 0 0
#> [21,] 0 0
#> [22,] 0 0
#> [23,] 0 0
#> [24,] 0 0
#> [25,] 0 0
#> [26,] 0 0
#> [27,] 0 0
#> [28,] 0 0
#> [29,] 0 0
#> [30,] 0 0
#> [31,] 0 0
#> [32,] 0 0
#> [33,] 0 0
#> [34,] 0 0
#> [35,] 0 0
#> [36,] 0 0
#> [37,] 0 0
#> [38,] 0 0
#> [39,] 0 0
#> [40,] 0 0
#> [41,] 0 0
#> [42,] 0 0
#> [43,] 0 0
#> [44,] 0 0
#> [45,] 0 0
#> [46,] 0 0
#> [47,] 0 0
#> [48,] 0 0
#> [49,] 0 0
#> [50,] 0 0
#> [51,] 0 0
#> [52,] 0 0
#> [53,] 0 0
#> [54,] 0 0
#> [55,] 0 0
#> [56,] 0 0
#> [57,] 0 0
#> [58,] 0 0
#> [59,] 0 0
#> [60,] 0 0
#> [61,] 0 0
#> [62,] 0 0
#> [63,] 0 0
#> [64,] 0 0
#> [65,] 0 0
#> [66,] 0 0
#> [67,] 0 0
#> [68,] 0 0
#> [69,] 0 0
#> [70,] 0 0
#> [71,] 0 0
#> [72,] 0 0
#> [73,] 0 0
#> [74,] 0 0
#> [75,] 0 0
#> [76,] 0 0
#> [77,] 0 0
#> [78,] 0 0
#> [79,] 0 0
#> [80,] 0 0
#> [81,] 0 0
#> [82,] 0 0
#> [83,] 0 0
#> [84,] 0 0
#> [85,] 0 0
#> [86,] 0 0
#> [87,] 0 0
#> [88,] 0 0
#> [89,] 0 0
#> [90,] 0 0
#> [91,] 0 0
#> [92,] 0 0
#> [93,] 0 0
#> [94,] 0 0
#> [95,] 0 0
#> [96,] 0 0
#> [97,] 0 0
#> [98,] 0 0
#> [99,] 0 0
#> [100,] 0 0
#> [101,] 0 0
#> [102,] 0 0
#> [103,] 0 0
#> [104,] 0 0
#> [105,] 0 0
#> [106,] 0 0
#> [107,] 0 0
#> [108,] 0 0
#> [109,] 0 0
#> [110,] 0 0
#> [111,] 0 0
#> [112,] 0 0
#> [113,] 0 0
#> [114,] 0 0
#> [115,] 0 0
#> [116,] 0 0
#> [117,] 0 0
#> [118,] 0 0
#> [119,] 0 0
#> [120,] 0 0
#> [121,] 0 0
#> [122,] 0 0
#> [123,] 0 0
#> [124,] 0 0
#> [125,] 0 0
#> [126,] 0 0
#> [127,] 0 0
#> [128,] 0 0
#> [129,] 0 0
#> [130,] 0 0
#> [131,] 0 0
#> [132,] 0 0
#> [133,] 0 0
#> [134,] 0 0
#> [135,] 0 0
#> [136,] 0 0
#> [137,] 0 0
#> [138,] 0 0
#> [139,] 0 0
#> [140,] 0 0
#> [141,] 0 0
#> [142,] 0 0
#> [143,] 0 0
#> [144,] 0 0
#> [145,] 0 0
#> [146,] 0 0
#> [147,] 0 0
#> [148,] 0 0
#> [149,] 0 0
#> [150,] 0 0
#> [151,] 0 0
#> [152,] 0 0
#> [153,] 0 0
#> [154,] 0 0
#> [155,] 0 0
#> [156,] 0 0
#> [157,] 0 0
#> [158,] 0 0
#> [159,] 0 0
#> [160,] 0 0
#> [161,] 0 0
#> [162,] 0 0
#> [163,] 0 0
#> [164,] 0 0
#> [165,] 0 0
#> [166,] 0 0
#> [167,] 0 0
#> [168,] 0 0
#> [169,] 0 0
#> [170,] 0 0
#> [171,] 0 0
#> [172,] 0 0
#> [173,] 0 0
#> [174,] 0 0
#> [175,] 0 0
#> [176,] 0 0
#> [177,] 0 0
#> [178,] 0 0
#> [179,] 0 0
#> [180,] 0 0
#> [181,] 0 0
#> [182,] 0 0
#> [183,] 0 0
#> [184,] 0 0
#> [185,] 0 0
#> [186,] 0 0
#> [187,] 0 0
#> [188,] 0 0
#> [189,] 0 0
#> [190,] 0 0
#> [191,] 0 0
#> [192,] 0 0
#> [193,] 0 0
#> [194,] 0 0
#> [195,] 0 0
#> [196,] 0 0
#> [197,] 0 0
#> [198,] 0 0
#> [199,] 0 0
#> [200,] 0 0
#> [201,] 0 0
#> [202,] 0 0
#> [203,] 0 0
#> [204,] 0 0
#> [205,] 0 0
#> [206,] 0 0
#> [207,] 0 0
#> [208,] 0 0
#> [209,] 0 0
#> [210,] 0 0
#> [211,] 0 0
#> [212,] 0 0
#> [213,] 0 0
#> [214,] 0 0
#> [215,] 0 0
#> [216,] 0 0
#> [217,] 0 0
#> [218,] 0 0
#> [219,] 0 1
#> [220,] 1 0
#> [221,] 0 0
#> [222,] 0 0
#> [223,] 0 0
#> [224,] 0 0
#> [225,] 0 0
#> [226,] 0 0
#> [227,] 0 0
#> [228,] 0 0
#> [229,] 0 0
#> [230,] 0 0
#> [231,] 0 0
#> [232,] 0 0
#> [233,] 0 0
#> [234,] 0 0
#> [235,] 0 0
#> [236,] 0 0
#> [237,] 0 0
#> [238,] 0 0
#> [239,] 0 0
#> [240,] 0 0
#> [241,] 0 0
#> [242,] 0 0
#> [243,] 0 0
#> [244,] 0 0
#> [245,] 0 0
#> [246,] 0 0
#> [247,] 0 0
#> [248,] 0 0
#> [249,] 0 0
#> [250,] 0 0
#> [251,] 0 0
#> [252,] 0 0
#> [253,] 0 0
#> [254,] 0 0
#> [255,] 0 0
#> [256,] 0 0
#> [257,] 0 0
#> [258,] 0 0
#> [259,] 0 0
#> [260,] 0 0
#> [261,] 0 0
#> [262,] 0 0
#> [263,] 0 0
#> [264,] 0 0
#> [265,] 0 0
#> [266,] 0 0
#> [267,] 0 0
#> [268,] 0 0
#> [269,] 0 0
#> [270,] 0 0
#> [271,] 0 0
#> [272,] 0 0
#> [273,] 0 0
#> [274,] 0 0
#> [275,] 0 0
#> [276,] 0 0
#> [277,] 0 0
#> [278,] 0 0
#> [279,] 0 0
#> [280,] 0 0
#> [281,] 0 0
#> [282,] 0 0
#> [283,] 0 0
#> [284,] 0 0
#> [285,] 0 0
#> [286,] 0 0
#> [287,] 0 0
#> [288,] 0 0
#> [289,] 0 0
#> [290,] 0 0
#> [291,] 0 0
#> [292,] 0 0
#> [293,] 0 0
#> [294,] 0 0
#> [295,] 0 0
#> [296,] 0 0
#> [297,] 0 0
#> [298,] 0 0
#> [299,] 0 0
#> [300,] 0 0
#> [301,] 0 0
#> [302,] 0 0
#> [303,] 0 0
#> [304,] 0 0
#> [305,] 0 0
#> [306,] 0 0
#> [307,] 0 0
#> [308,] 0 0
#> [309,] 0 0
#> [310,] 0 0
#> [311,] 0 0
#> [312,] 0 0
#> [313,] 0 0
#> [314,] 0 0
#> [315,] 0 0
#> [316,] 0 0
#> [317,] 0 0
#> [318,] 0 0
#> [319,] 0 0
#> [320,] 0 0
#> [321,] 0 0
#> [322,] 0 0
#> [323,] 0 0
#> [324,] 0 0
#> [325,] 0 0
#> [326,] 0 0
#> [327,] 0 0
#> [328,] 0 0
#> [329,] 0 0
#> [330,] 0 0
#> [331,] 0 0
#> [332,] 0 0
#> [333,] 0 0
#> [334,] 0 0
#> [335,] 0 0
#> [336,] 0 0
#> [337,] 0 0
#> [338,] 0 0
#> [339,] 0 0
#> [340,] 0 0
#> [341,] 0 0
#> [342,] 0 0
#> [343,] 0 0
#> [344,] 0 0
#> [345,] 0 0
#> [346,] 0 0
#> [347,] 0 0
#> [348,] 0 0
#> [349,] 0 0
#> [350,] 0 0
#> [351,] 0 0
#> [352,] 0 0
#> [353,] 0 0
#> [354,] 0 0
#> [355,] 0 0
#> [356,] 0 0
#> [357,] 0 0
#> [358,] 0 0
#> [359,] 0 0
#> [360,] 0 0
#> [361,] 0 0
#> [362,] 0 0
#> [363,] 0 0
#> [364,] 0 0
#> [365,] 0 0
#> [366,] 0 0
#> [367,] 0 0
#> [368,] 0 0
#> [369,] 0 0
#> [370,] 0 0
#> [371,] 0 0
#> [372,] 0 0
#> [373,] 0 0
#> [374,] 0 0
#> [375,] 0 0
#> [376,] 0 0
#> [377,] 0 0
#> [378,] 0 0
#> [379,] 0 0
#> [380,] 0 0
#> [381,] 0 0
#> [382,] 0 0
#> [383,] 0 0
#> [384,] 0 0
#> [385,] 0 0
#> [386,] 0 0
#> [387,] 0 0
#> [388,] 0 0
#> [389,] 0 0
#> [390,] 0 0
#> [391,] 0 0
#> [392,] 0 0
#> [393,] 0 0
#> [394,] 0 0
#> [395,] 0 0
#> [396,] 0 0
#> [397,] 0 0
#> [398,] 0 0
#> [399,] 0 0
#> [400,] 0 0
#> [401,] 0 0
#> [402,] 0 0
#> [403,] 0 0
#> [404,] 0 0
#> [405,] 0 0
#> [406,] 0 0
#> [407,] 0 0
#> [408,] 0 0
#> [409,] 0 0
#> [410,] 0 0
#> [411,] 0 0
#> [412,] 0 0
#> [413,] 0 0
#> [414,] 0 0
#> [415,] 0 0
#> [416,] 0 0
#> [417,] 0 0
#> [418,] 0 0
#> [419,] 0 0
#> [420,] 0 0
#> [421,] 0 0
#> [422,] 0 0
#> [423,] 0 0
#> [424,] 0 0
#> [425,] 0 0
#>
#> $model$b
#> [1] -211.0035 189.6218
#>
#> $model$bcov
#> [,1] [,2]
#> [1,] 1516.9191 124.4537
#> [2,] 124.4537 1516.9191
#>
#> $model$linearized
#> [1] 460.1000 502.6000 443.8000 459.1000 438.4000 465.1000 452.7000
#> [8] 522.9000 889.3000 379.2000 378.0000 472.1000 503.4000 510.6000
#> [15] 462.4000 468.3000 458.2000 482.7000 485.3000 568.7000 963.7000
#> [22] 414.7000 414.5000 484.7000 487.3000 597.9000 500.4000 543.4000
#> [29] 503.4000 522.8000 556.6000 623.2000 1039.4000 516.3000 452.5000
#> [36] 525.8000 587.7000 700.3000 561.8000 602.8000 582.5000 563.1000
#> [43] 637.1000 697.1000 1187.5000 570.5000 478.2000 547.4000 594.3000
#> [50] 751.6000 553.4000 663.2000 581.1000 661.9000 665.6000 700.9000
#> [57] 1367.9000 613.9000 513.2000 599.9000 674.1000 714.0000 670.5000
#> [64] 720.9000 601.6000 672.3000 709.1000 743.2000 1460.1000 631.0000
#> [71] 551.1000 678.1000 715.7000 740.8000 722.0000 683.5000 650.9000
#> [78] 723.3000 729.6000 870.3000 1570.0000 631.5000 552.0000 719.0000
#> [85] 697.6000 764.8000 786.3000 715.1000 723.8000 757.9000 751.7000
#> [92] 923.8000 1569.4000 678.2000 586.2000 726.8000 744.1000 815.5000
#> [99] 832.4000 710.3000 759.4000 741.1000 786.6000 931.5000 1563.1000
#> [106] 694.0000 604.7000 719.2000 748.2000 828.2000 746.9000 794.5000
#> [113] 770.4000 741.5000 858.6000 944.7000 1600.3000 740.0000 665.9000
#> [120] 701.5000 831.4000 878.6000 826.0000 788.2000 723.6000 819.8000
#> [127] 902.5000 968.6000 1650.9000 762.1000 643.0000 754.1000 840.7000
#> [134] 906.6000 887.1000 771.5000 728.7000 844.7000 886.9000 970.0000
#> [141] 1710.5000 745.7000 664.4000 821.5000 831.7000 908.0000 912.6000
#> [148] 782.9000 798.8000 887.0000 934.6000 1000.4000 1817.5000 752.4000
#> [155] 682.5000 811.2000 906.0000 927.2000 906.8000 880.6000 873.9000
#> [162] 856.8000 920.6000 1067.4000 1857.2000 833.1000 737.1000 812.0000
#> [169] 895.2000 962.8000 908.6000 908.0000 888.9000 833.7000 933.7000
#> [176] 1081.6000 1837.6000 840.9000 727.4000 857.9000 849.0000 994.8000
#> [183] 830.2000 971.1000 836.0000 939.1000 976.9000 1111.3000 1879.1000
#> [190] 917.3000 716.2000 822.9000 970.1000 970.2000 849.4000 1042.3000
#> [197] 869.9000 939.4000 1021.3000 1137.7000 1975.7000 942.0000 738.4000
#> [204] 903.2000 953.2000 1011.2000 894.4000 1054.5000 899.5000 1002.3000
#> [211] 1043.7000 1207.2000 2069.6000 924.9000 798.2000 901.9000 1024.7000
#> [218] 1052.3000 975.8782 1070.3035 1009.2000 1054.6000 1070.4000 1232.5000
#> [225] 2177.5000 971.9000 814.6000 1017.5000 1039.2000 1123.5000 1024.9000
#> [232] 1100.8000 963.0000 1012.9000 1132.0000 1344.8000 2269.5000 1027.9000
#> [239] 841.4000 1043.9000 1075.3000 1190.9000 1143.0000 1075.7000 1065.9000
#> [246] 1060.1000 1211.4000 1495.1000 2338.6000 1099.3000 900.5000 1092.7000
#> [253] 1222.4000 1237.1000 1237.9000 1182.0000 1101.2000 1198.2000 1316.1000
#> [260] 1528.2000 2424.2000 1182.9000 989.8000 1131.4000 1277.1000 1280.3000
#> [267] 1384.1000 1305.9000 1166.8000 1317.9000 1358.3000 1536.7000 2500.8000
#> [274] 1246.3000 1037.3000 1300.8000 1153.7000 1264.2000 1454.2000 1290.1000
#> [281] 1210.7000 1277.8000 1314.4000 1540.4000 2536.0000 1193.7000 1037.7000
#> [288] 1204.5000 1348.6000 1267.6000 1429.0000 1412.0000 1239.2000 1219.1000
#> [295] 1344.6000 1623.3000 2611.1000 1267.3000 1047.0000 1331.6000 1302.6000
#> [302] 1365.1000 1491.5000 1462.3000 1315.5000 1353.3000 1440.6000 1687.9000
#> [309] 2747.0000 1397.8000 1140.5000 1351.7000 1396.6000 1421.1000 1401.6000
#> [316] 1582.3000 1268.4000 1383.3000 1452.4000 1675.9000 2886.1000 1451.0000
#> [323] 1056.6000 1386.9000 1509.1000 1519.4000 1500.5000 1570.7000 1341.5000
#> [330] 1399.9000 1534.3000 1736.6000 2795.1000 1469.1000 1111.9000 1379.9000
#> [337] 1389.7000 1427.2000 1551.4000 1581.0000 1324.0000 1422.0000 1464.9000
#> [344] 1705.5000 2752.4000 1412.6000 1117.5000 1321.6000 1472.6000 1408.9000
#> [351] 1471.9000 1532.5000 1293.5000 1345.7000 1404.7000 1660.0000 2730.5000
#> [358] 1362.4000 1131.7000 1349.2000 1391.2000 1456.9000 1616.4000 1423.4000
#> [365] 1359.0000 1367.8000 1442.6000 1672.9000 2753.3000 1397.4000 1113.6000
#> [372] 1397.3000 1339.1000 1441.9000 1537.4000 1390.6000 1337.2000 1359.4000
#> [379] 1463.3000 1668.9000 2725.5000 1451.0000 1064.9000 1293.2000 1442.9000
#> [386] 1411.8000 1461.6000 1501.6000 1254.2000 1356.4000 1478.7000 1687.7000
#> [393] 2756.9000 1471.2000 1053.8000 1367.2000 1442.2000 1428.7000 1480.9000
#> [400] 1540.9000 1331.9000 1400.1000 1566.3000 1730.5000 2913.6000 1519.2000
#> [407] 1155.8000 1451.5000 1451.0000 1449.7000 1596.1000 1468.3000 1293.9000
#> [414] 1393.5000 1497.4000 1684.3000 2850.4000 1428.5000 1092.4000 1370.3000
#> [421] 1522.6000 1452.4000 1557.2000 1445.5000 1303.1000
#>
#>
#> $likelihood
#> $likelihood$initial
#> $likelihood$initial$ll
#> [1] -2218.964
#>
#> $likelihood$initial$ssq
#> [1] 1139531
#>
#> $likelihood$initial$nobs
#> [1] 425
#>
#> $likelihood$initial$neffective
#> [1] -1
#>
#> $likelihood$initial$nparams
#> [1] 3
#>
#> $likelihood$initial$df
#> [1] 409
#>
#> $likelihood$initial$aic
#> [1] 4443.928
#>
#> $likelihood$initial$aicc
#> [1] 4443.987
#>
#> $likelihood$initial$bic
#> [1] 4455.991
#>
#> $likelihood$initial$bic2
#> [1] 10.81551
#>
#> $likelihood$initial$bicc
#> [1] 7.954332
#>
#> $likelihood$initial$hannanquinn
#> [1] 4448.7
#>
#>
#> $likelihood$final
#> $likelihood$final$ll
#> [1] -2194.499
#>
#> $likelihood$final$ssq
#> [1] 1014171
#>
#> $likelihood$final$nobs
#> [1] 425
#>
#> $likelihood$final$neffective
#> [1] -1
#>
#> $likelihood$final$nparams
#> [1] 3
#>
#> $likelihood$final$df
#> [1] 409
#>
#> $likelihood$final$aic
#> [1] 4394.998
#>
#> $likelihood$final$aicc
#> [1] 4395.057
#>
#> $likelihood$final$bic
#> [1] 4407.061
#>
#> $likelihood$final$bic2
#> [1] 10.69675
#>
#> $likelihood$final$bicc
#> [1] 7.837787
#>
#> $likelihood$final$hannanquinn
#> [1] 4399.77
#>
#>
#>
#> attr(,"class")
#> [1] "JD3_REGARIMA_OUTLIERS"