Function to generate the sequence of lambdas
Arguments
- YStd
Numeric matrix. Matrix of standardized dependent variables (Y).
- XStd
Numeric matrix. Matrix of standardized predictors (X).
XStdshould not include a vector of ones in column one.- n_lambdas
Integer. Number of lambdas to generate.
See also
Other Fitting Autoregressive Model Functions:
FitMLVARDynr(),
FitMLVARMplus(),
FitVARDynr(),
FitVARLassoSearch(),
FitVARLasso(),
FitVARMplus(),
FitVAROLS(),
ModelVARP1Dynr(),
ModelVARP2Dynr(),
OrigScale(),
PBootVARExoLasso(),
PBootVARExoOLS(),
PBootVARLasso(),
PBootVAROLS(),
RBootVARExoLasso(),
RBootVARExoOLS(),
RBootVARLasso(),
RBootVAROLS(),
SearchVARLasso(),
StdMat()
Examples
YStd <- StdMat(dat_p2_yx$Y)
XStd <- StdMat(dat_p2_yx$X[, -1]) # remove the constant column
LambdaSeq(YStd = YStd, XStd = XStd, n_lambdas = 100)
#> [,1]
#> [1,] 166.1666667
#> [2,] 154.9675895
#> [3,] 144.5232926
#> [4,] 134.7829064
#> [5,] 125.6989896
#> [6,] 117.2272984
#> [7,] 109.3265708
#> [8,] 101.9583259
#> [9,] 95.0866759
#> [10,] 88.6781522
#> [11,] 82.7015416
#> [12,] 77.1277345
#> [13,] 71.9295833
#> [14,] 67.0817701
#> [15,] 62.5606833
#> [16,] 58.3443027
#> [17,] 54.4120919
#> [18,] 50.7448990
#> [19,] 47.3248627
#> [20,] 44.1353253
#> [21,] 41.1607521
#> [22,] 38.3866552
#> [23,] 35.7995231
#> [24,] 33.3867551
#> [25,] 31.1365995
#> [26,] 29.0380969
#> [27,] 27.0810264
#> [28,] 25.2558558
#> [29,] 23.5536957
#> [30,] 21.9662554
#> [31,] 20.4858033
#> [32,] 19.1051287
#> [33,] 17.8175070
#> [34,] 16.6166667
#> [35,] 15.4967589
#> [36,] 14.4523293
#> [37,] 13.4782906
#> [38,] 12.5698990
#> [39,] 11.7227298
#> [40,] 10.9326571
#> [41,] 10.1958326
#> [42,] 9.5086676
#> [43,] 8.8678152
#> [44,] 8.2701542
#> [45,] 7.7127734
#> [46,] 7.1929583
#> [47,] 6.7081770
#> [48,] 6.2560683
#> [49,] 5.8344303
#> [50,] 5.4412092
#> [51,] 5.0744899
#> [52,] 4.7324863
#> [53,] 4.4135325
#> [54,] 4.1160752
#> [55,] 3.8386655
#> [56,] 3.5799523
#> [57,] 3.3386755
#> [58,] 3.1136600
#> [59,] 2.9038097
#> [60,] 2.7081026
#> [61,] 2.5255856
#> [62,] 2.3553696
#> [63,] 2.1966255
#> [64,] 2.0485803
#> [65,] 1.9105129
#> [66,] 1.7817507
#> [67,] 1.6616667
#> [68,] 1.5496759
#> [69,] 1.4452329
#> [70,] 1.3478291
#> [71,] 1.2569899
#> [72,] 1.1722730
#> [73,] 1.0932657
#> [74,] 1.0195833
#> [75,] 0.9508668
#> [76,] 0.8867815
#> [77,] 0.8270154
#> [78,] 0.7712773
#> [79,] 0.7192958
#> [80,] 0.6708177
#> [81,] 0.6256068
#> [82,] 0.5834430
#> [83,] 0.5441209
#> [84,] 0.5074490
#> [85,] 0.4732486
#> [86,] 0.4413533
#> [87,] 0.4116075
#> [88,] 0.3838666
#> [89,] 0.3579952
#> [90,] 0.3338676
#> [91,] 0.3113660
#> [92,] 0.2903810
#> [93,] 0.2708103
#> [94,] 0.2525586
#> [95,] 0.2355370
#> [96,] 0.2196626
#> [97,] 0.2048580
#> [98,] 0.1910513
#> [99,] 0.1781751
#> [100,] 0.1661667