simulate.midas_r: Simulate MIDAS regression response
In midasr: Mixed Data Sampling Regression
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Simulates one or more responses from the distribution corresponding to a fitted MIDAS regression object.
Usage
1
2
3
4
5
6
7
8
9
10
11
12
13
Arguments
object
midas_r object
nsim
number of simulations
seed
either NULL or an integer that will be used in a call to set.seed before simulating the time series. The default, NULL will not change the random generator state.
future
logical, if TRUE forecasts are simulated, if FALSE in-sample simulation is performed.
newdata
a named list containing future values of mixed frequency regressors. The default is NULL, meaning that only in-sample data is used.
insample
a list containing the historic mixed frequency data
method
the simulation method, if "static" in-sample values for dependent variable are used in autoregressive MIDAS model, if "dynamic"
the dependent variable values are calculated step-by-step from the initial in-sample values.
innov
a matrix containing the simulated innovations. The default is NULL, meaning, that innovations are simulated from model residuals.
show_progress
logical, TRUE to show progress bar, FALSE for silent evaluation
...
not used currently
Details
Only the regression innovations are simulated, it is assumed that the predictor variables and coefficients are fixed. The
innovation distribution is simulated via bootstrap.
Value
a matrix of simulated responses. Each row contains a simulated response.
Author(s)
Virmantas Kvedaras, Vaidotas Zemlys
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
data("USrealgdp")
data("USunempr")
y <- diff(log(USrealgdp))
x <- window(diff(USunempr), start = 1949)
trend <- 1:length(y)
##24 high frequency lags of x included
mr <- midas_r(y ~ trend + fmls(x, 23, 12, nealmon), start = list(x = rep(0, 3)))
simulate(mr, nsim=10, future=FALSE)
##Forecast horizon
h <- 3
##Declining unemployment
xn <- rep(-0.1, 12*3)
##New trend values
trendn <- length(y) + 1:h
simulate(mr, nsim = 10, future = TRUE, newdata = list(trend = trendn, x = xn))
Example output
Loading required package: sandwich
Loading required package: optimx
Loading required package: quantreg
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
[,1] [,2] [,3] [,4] [,5]
[1,] 0.086994161 0.066145627 0.077464784 0.0929377160 0.080313030
[2,] 0.076620783 0.075185495 0.066631245 0.0624687389 0.077191839
[3,] 0.051310380 0.062464468 0.055245941 0.0386820023 0.053814758
[4,] 0.036213859 0.033347654 0.047345485 0.0511990671 0.037153749
[5,] -0.001737986 -0.012905160 -0.003285350 -0.0027537285 -0.007987541
[6,] 0.072723425 0.072723425 0.062506941 0.0775668202 0.076253634
[7,] 0.041648970 0.041648970 0.042237456 0.0451737093 0.044330175
[8,] 0.034276184 0.036529207 0.013022506 0.0389506529 0.022124376
[9,] -0.004308391 -0.004492701 -0.005598101 -0.0007363188 -0.003776769
[10,] 0.081740392 0.062826931 0.070676120 0.0578202112 0.067695075
[11,] 0.035071397 0.011272580 0.039044721 0.0350713967 0.029055183
[12,] 0.021411697 0.006976010 0.023067474 0.0305559645 0.028335644
[13,] 0.050572336 0.064220096 0.061201872 0.0544499953 0.044456453
[14,] 0.035853232 0.043449719 0.043526227 0.0412907248 0.019943018
[15,] 0.047657481 0.033728994 0.053732385 0.0471938350 0.046164421
[16,] 0.057342328 0.044696953 0.051089803 0.0510898035 0.053081813
[17,] 0.041772118 0.045686855 0.051950838 0.0564952182 0.037469499
[18,] 0.036988889 0.039081608 0.019965050 0.0324366521 0.014332700
[19,] 0.058426401 0.022887672 0.037187963 0.0360536019 0.042448510
[20,] 0.027119399 0.032995901 0.033446031 0.0295312937 0.042766001
[21,] -0.008454706 -0.003826541 -0.001174668 0.0090170673 0.012870650
[22,] 0.016783835 0.028357335 0.034040195 0.0178268468 0.028357335
[23,] 0.039137572 0.036725678 0.043063767 0.0493162913 0.051858433
[24,] 0.050008192 0.046087256 0.054793124 0.0548757594 0.050914283
[25,] 0.016980724 0.014143954 0.009665344 0.0139402741 0.019435445
[26,] -0.013128855 -0.014283077 -0.011416873 -0.0083215213 -0.002110419
[27,] 0.049918102 0.054139291 0.046937057 0.0428845028 0.049276828
[28,] 0.065666699 0.049224605 0.056969721 0.0609052817 0.050667975
[29,] 0.052901013 0.057795597 0.047667201 0.0459770660 0.043829183
[30,] 0.040176826 0.039076666 0.032431710 0.0274936874 0.049866486
[31,] -0.002755884 0.002767383 -0.001043901 0.0044679542 -0.006884301
[32,] 0.033874930 0.016147349 0.009421858 0.0111538343 0.027589187
[33,] -0.011259856 -0.013415317 -0.019615754 -0.0174678707 -0.010434272
[34,] 0.055696170 0.061209116 0.061209116 0.0626402985 0.038488020
[35,] 0.063559908 0.078310864 0.061903127 0.0666053172 0.070378928
[36,] 0.028642357 0.034355093 0.018708146 0.0312559460 0.026930689
[37,] 0.024889079 0.017459488 0.030007288 0.0433437427 0.036399614
[38,] 0.029260171 0.055874281 0.033160545 0.0448927555 0.052835972
[39,] 0.046322328 0.040277207 0.047142457 0.0505145138 0.044305687
[40,] 0.041517425 0.035308598 0.029581652 0.0353085982 0.036216281
[41,] 0.028415381 0.010366069 0.012256794 0.0096619685 0.029391788
[42,] 0.009396707 0.013943115 0.001049442 -0.0087706215 0.006426196
[43,] 0.034828892 0.028518200 0.015893514 0.0198934389 0.014520369
[44,] 0.036215352 0.042042235 0.048229709 0.0381274978 0.059498070
[45,] 0.048273623 0.046706649 0.056561605 0.0442736979 0.028434016
[46,] 0.040996764 0.037209929 0.037466555 0.0350992601 0.029685264
[47,] 0.041886718 0.032578205 0.026240116 0.0428040542 0.041886718
[48,] 0.021655821 0.035823224 0.034203621 0.0283813114 0.028081629
[49,] 0.050253317 0.015262648 0.029698335 0.0264160899 0.035906350
[50,] 0.037671568 0.032393966 0.037671568 0.0125988753 0.028231460
[51,] 0.026886457 0.012419919 0.032259527 0.0283301332 0.044737870
[52,] 0.021329407 0.003423443 0.005877041 -0.0117733746 0.007902053
[53,] 0.011940436 0.012174689 0.006100036 -0.0019880515 0.017128506
[54,] 0.029103898 0.030732285 0.037950813 0.0146680947 0.024644074
[55,] 0.020628473 0.040807427 0.039140140 0.0188964974 0.039383987
[56,] 0.043994396 0.027835809 0.034162440 0.0423180806 0.039098603
[57,] 0.041414808 0.015077955 0.015077955 0.0186978174 0.031203776
[58,] 0.028514270 0.025560216 0.026660376 0.0236518077 0.018660163
[59,] -0.008063662 -0.014138314 -0.014692823 -0.0172328878 -0.008297915
[60,] -0.042997153 -0.051768162 -0.036458604 -0.0354802342 -0.037021652
[61,] 0.027509951 0.025113174 0.012641572 0.0265565435 0.032744018
[62,] 0.025516645 0.043457167 0.031885095 0.0410017661 0.040782904
[,6] [,7] [,8] [,9] [,10]
[1,] 0.062245252 0.0856173739 0.062245252 8.594167e-02 0.0786934267
[2,] 0.075189600 0.0611364455 0.068386944 7.242860e-02 0.0507365286
[3,] 0.053617554 0.0397616033 0.052075423 3.758423e-02 0.0310936625
[4,] 0.042288762 0.0422887622 0.041401929 1.665302e-02 0.0423878166
[5,] 0.010877306 -0.0079875410 0.002227596 2.223491e-03 -0.0032853503
[6,] 0.060594795 0.0724667989 0.049102452 7.268566e-02 0.0732869281
[7,] 0.021809362 0.0479347131 0.047847972 3.341585e-02 0.0299074497
[8,] 0.030355249 0.0374085217 0.035867104 4.534046e-02 0.0298916026
[9,] -0.005081187 -0.0005347797 -0.016822708 -2.151654e-02 -0.0037767688
[10,] 0.084197503 0.0662915378 0.057520529 7.158221e-02 0.0494224416
[11,] 0.038639371 0.0195862755 0.029760821 3.567266e-02 0.0319162821
[12,] 0.010595873 0.0285393238 0.018129452 3.331286e-02 0.0231018312
[13,] 0.045978669 0.0642200957 0.061201872 4.918739e-02 0.0456789865
[14,] 0.044329034 0.0268185727 0.046582215 4.541789e-02 0.0197274107
[15,] 0.046234952 0.0557996057 0.058789107 4.372923e-02 0.0471938350
[16,] 0.053081813 0.0453656091 0.057380092 3.354351e-02 0.0496349758
[17,] 0.039974119 0.0466402623 0.054488874 5.177528e-02 0.0542957757
[18,] 0.037814473 0.0378144734 0.029965285 4.074527e-02 0.0199650503
[19,] 0.034217637 0.0285200226 0.033513537 3.672226e-02 0.0522669499
[20,] 0.039534450 0.0405128197 0.042165414 3.344603e-02 0.0484447553
[21,] -0.010345431 0.0066739442 -0.010345431 -9.552479e-03 0.0041359076
[22,] 0.030509985 0.0392069633 0.010293268 3.920696e-02 0.0267286200
[23,] 0.043052309 0.0472014048 0.032608637 3.413085e-02 0.0366709170
[24,] 0.047027147 0.0460872564 0.062065147 4.036100e-02 0.0559056966
[25,] 0.012118942 0.0008943353 0.012635855 1.011547e-02 0.0070163270
[26,] -0.002110419 -0.0104769821 -0.011416873 -1.143039e-02 -0.0072812932
[27,] 0.042068913 0.0320686788 0.040736620 4.553352e-02 0.0367625111
[28,] 0.054696455 0.0417465180 0.051621382 5.066797e-02 0.0599879450
[29,] 0.044748424 0.0486260832 0.038632541 6.407494e-02 0.0427495818
[30,] 0.014327758 0.0371682574 0.038024175 3.412285e-02 0.0446834421
[31,] 0.006009372 -0.0092747737 0.002971063 4.467954e-03 -0.0001040104
[32,] 0.028893605 0.0158476667 0.023659793 2.602221e-02 0.0193560693
[33,] -0.008181249 -0.0045367674 -0.005649340 -8.280304e-03 -0.0174678707
[34,] 0.034868157 0.0493038443 0.043481535 5.859047e-02 0.0348681574
[35,] 0.068837511 0.0666053172 0.080767974 4.766519e-02 0.0671369390
[36,] 0.017035867 0.0148077716 0.028528210 3.435509e-02 0.0381798931
[37,] 0.035626817 0.0362153033 0.036399614 2.919170e-02 0.0287786461
[38,] 0.049760899 0.0662633272 0.041383088 5.812746e-02 0.0581274627
[39,] 0.045478793 0.0350301764 0.033950575 4.734400e-02 0.0465411891
[40,] 0.040600088 0.0254532349 0.035104918 3.152789e-02 0.0352145700
[41,] 0.004668454 0.0254486750 0.022408225 1.548428e-02 0.0185948833
[42,] 0.011812489 0.0068877839 0.006426196 -8.770622e-03 0.0068877839
[43,] 0.026175077 0.0281814204 0.022574644 -2.174264e-03 0.0323717822
[44,] 0.041592105 0.0391895463 0.051362205 3.812750e-02 0.0451511025
[45,] 0.045753242 0.0418385049 0.050593785 3.992636e-02 0.0509548285
[46,] 0.013629974 0.0311648081 0.028312120 4.715622e-02 0.0138455819
[47,] 0.038868494 0.0251423431 0.018651776 2.971406e-02 0.0388307296
[48,] 0.030976137 0.0547586029 0.036852638 3.445008e-02 0.0373142263
[49,] 0.031099015 0.0376380247 0.042629438 2.969833e-02 0.0132501602
[50,] 0.037671568 0.0164992497 0.037671568 4.095232e-02 0.0376715685
[51,] 0.010191824 0.0124199195 0.024492115 3.526452e-02 0.0385452703
[52,] 0.013193543 0.0198649795 0.010940362 -4.116437e-05 0.0034234425
[53,] 0.007599130 0.0104473757 0.006679889 6.679889e-03 0.0167674625
[54,] 0.014113586 0.0244294299 0.020494978 1.908597e-02 0.0152479477
[55,] 0.025621988 0.0331344320 0.017224218 2.409977e-02 0.0453518074
[56,] 0.023718768 0.0341738981 0.034162440 3.936403e-02 0.0302477030
[57,] 0.035133170 0.0381381602 0.047611513 2.439543e-02 0.0476115132
[58,] 0.015111599 0.0160308402 0.036350037 1.894962e-02 0.0264470494
[59,] -0.014692823 -0.0021239571 -0.009754801 -5.791050e-03 -0.0020474488
[60,] -0.037345437 -0.0600818573 -0.045646170 -4.159362e-02 -0.0529906954
[61,] 0.026106414 0.0415552682 0.042547951 4.401238e-02 0.0326449634
[62,] 0.027313380 0.0440577540 0.033258240 3.784665e-02 0.0258163271
[,1] [,2] [,3]
[1,] 0.05157169 0.05353564 0.04533419
[2,] 0.03966726 0.05117484 0.04064836
[3,] 0.05389255 0.05812013 0.04952909
[4,] 0.05302011 0.04397099 0.04923552
[5,] 0.03716264 0.04819529 0.04306025
[6,] 0.04923552 0.05316247 0.03905337
[7,] 0.05558391 0.04105221 0.03870503
[8,] 0.04228085 0.02776720 0.02944016
[9,] 0.04891122 0.06296640 0.05142512
[10,] 0.04064836 0.04848887 0.04968312
midasr documentation built on Feb. 23, 2021, 5:11 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Simulates one or more responses from the distribution corresponding to a fitted MIDAS regression object.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 |
Arguments
object |
|
nsim |
number of simulations |
seed |
either NULL or an integer that will be used in a call to set.seed before simulating the time series. The default, NULL will not change the random generator state. |
future |
logical, if |
newdata |
a named list containing future values of mixed frequency regressors. The default is |
insample |
a list containing the historic mixed frequency data |
method |
the simulation method, if |
innov |
a matrix containing the simulated innovations. The default is |
show_progress |
logical, TRUE to show progress bar, FALSE for silent evaluation |
... |
not used currently |
Details
Only the regression innovations are simulated, it is assumed that the predictor variables and coefficients are fixed. The innovation distribution is simulated via bootstrap.
Value
a matrix of simulated responses. Each row contains a simulated response.
Author(s)
Virmantas Kvedaras, Vaidotas Zemlys
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | data("USrealgdp")
data("USunempr")
y <- diff(log(USrealgdp))
x <- window(diff(USunempr), start = 1949)
trend <- 1:length(y)
##24 high frequency lags of x included
mr <- midas_r(y ~ trend + fmls(x, 23, 12, nealmon), start = list(x = rep(0, 3)))
simulate(mr, nsim=10, future=FALSE)
##Forecast horizon
h <- 3
##Declining unemployment
xn <- rep(-0.1, 12*3)
##New trend values
trendn <- length(y) + 1:h
simulate(mr, nsim = 10, future = TRUE, newdata = list(trend = trendn, x = xn))
|
Example output
Loading required package: sandwich
Loading required package: optimx
Loading required package: quantreg
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
[,1] [,2] [,3] [,4] [,5]
[1,] 0.086994161 0.066145627 0.077464784 0.0929377160 0.080313030
[2,] 0.076620783 0.075185495 0.066631245 0.0624687389 0.077191839
[3,] 0.051310380 0.062464468 0.055245941 0.0386820023 0.053814758
[4,] 0.036213859 0.033347654 0.047345485 0.0511990671 0.037153749
[5,] -0.001737986 -0.012905160 -0.003285350 -0.0027537285 -0.007987541
[6,] 0.072723425 0.072723425 0.062506941 0.0775668202 0.076253634
[7,] 0.041648970 0.041648970 0.042237456 0.0451737093 0.044330175
[8,] 0.034276184 0.036529207 0.013022506 0.0389506529 0.022124376
[9,] -0.004308391 -0.004492701 -0.005598101 -0.0007363188 -0.003776769
[10,] 0.081740392 0.062826931 0.070676120 0.0578202112 0.067695075
[11,] 0.035071397 0.011272580 0.039044721 0.0350713967 0.029055183
[12,] 0.021411697 0.006976010 0.023067474 0.0305559645 0.028335644
[13,] 0.050572336 0.064220096 0.061201872 0.0544499953 0.044456453
[14,] 0.035853232 0.043449719 0.043526227 0.0412907248 0.019943018
[15,] 0.047657481 0.033728994 0.053732385 0.0471938350 0.046164421
[16,] 0.057342328 0.044696953 0.051089803 0.0510898035 0.053081813
[17,] 0.041772118 0.045686855 0.051950838 0.0564952182 0.037469499
[18,] 0.036988889 0.039081608 0.019965050 0.0324366521 0.014332700
[19,] 0.058426401 0.022887672 0.037187963 0.0360536019 0.042448510
[20,] 0.027119399 0.032995901 0.033446031 0.0295312937 0.042766001
[21,] -0.008454706 -0.003826541 -0.001174668 0.0090170673 0.012870650
[22,] 0.016783835 0.028357335 0.034040195 0.0178268468 0.028357335
[23,] 0.039137572 0.036725678 0.043063767 0.0493162913 0.051858433
[24,] 0.050008192 0.046087256 0.054793124 0.0548757594 0.050914283
[25,] 0.016980724 0.014143954 0.009665344 0.0139402741 0.019435445
[26,] -0.013128855 -0.014283077 -0.011416873 -0.0083215213 -0.002110419
[27,] 0.049918102 0.054139291 0.046937057 0.0428845028 0.049276828
[28,] 0.065666699 0.049224605 0.056969721 0.0609052817 0.050667975
[29,] 0.052901013 0.057795597 0.047667201 0.0459770660 0.043829183
[30,] 0.040176826 0.039076666 0.032431710 0.0274936874 0.049866486
[31,] -0.002755884 0.002767383 -0.001043901 0.0044679542 -0.006884301
[32,] 0.033874930 0.016147349 0.009421858 0.0111538343 0.027589187
[33,] -0.011259856 -0.013415317 -0.019615754 -0.0174678707 -0.010434272
[34,] 0.055696170 0.061209116 0.061209116 0.0626402985 0.038488020
[35,] 0.063559908 0.078310864 0.061903127 0.0666053172 0.070378928
[36,] 0.028642357 0.034355093 0.018708146 0.0312559460 0.026930689
[37,] 0.024889079 0.017459488 0.030007288 0.0433437427 0.036399614
[38,] 0.029260171 0.055874281 0.033160545 0.0448927555 0.052835972
[39,] 0.046322328 0.040277207 0.047142457 0.0505145138 0.044305687
[40,] 0.041517425 0.035308598 0.029581652 0.0353085982 0.036216281
[41,] 0.028415381 0.010366069 0.012256794 0.0096619685 0.029391788
[42,] 0.009396707 0.013943115 0.001049442 -0.0087706215 0.006426196
[43,] 0.034828892 0.028518200 0.015893514 0.0198934389 0.014520369
[44,] 0.036215352 0.042042235 0.048229709 0.0381274978 0.059498070
[45,] 0.048273623 0.046706649 0.056561605 0.0442736979 0.028434016
[46,] 0.040996764 0.037209929 0.037466555 0.0350992601 0.029685264
[47,] 0.041886718 0.032578205 0.026240116 0.0428040542 0.041886718
[48,] 0.021655821 0.035823224 0.034203621 0.0283813114 0.028081629
[49,] 0.050253317 0.015262648 0.029698335 0.0264160899 0.035906350
[50,] 0.037671568 0.032393966 0.037671568 0.0125988753 0.028231460
[51,] 0.026886457 0.012419919 0.032259527 0.0283301332 0.044737870
[52,] 0.021329407 0.003423443 0.005877041 -0.0117733746 0.007902053
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[12,] 0.010595873 0.0285393238 0.018129452 3.331286e-02 0.0231018312
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[38,] 0.049760899 0.0662633272 0.041383088 5.812746e-02 0.0581274627
[39,] 0.045478793 0.0350301764 0.033950575 4.734400e-02 0.0465411891
[40,] 0.040600088 0.0254532349 0.035104918 3.152789e-02 0.0352145700
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[42,] 0.011812489 0.0068877839 0.006426196 -8.770622e-03 0.0068877839
[43,] 0.026175077 0.0281814204 0.022574644 -2.174264e-03 0.0323717822
[44,] 0.041592105 0.0391895463 0.051362205 3.812750e-02 0.0451511025
[45,] 0.045753242 0.0418385049 0.050593785 3.992636e-02 0.0509548285
[46,] 0.013629974 0.0311648081 0.028312120 4.715622e-02 0.0138455819
[47,] 0.038868494 0.0251423431 0.018651776 2.971406e-02 0.0388307296
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[49,] 0.031099015 0.0376380247 0.042629438 2.969833e-02 0.0132501602
[50,] 0.037671568 0.0164992497 0.037671568 4.095232e-02 0.0376715685
[51,] 0.010191824 0.0124199195 0.024492115 3.526452e-02 0.0385452703
[52,] 0.013193543 0.0198649795 0.010940362 -4.116437e-05 0.0034234425
[53,] 0.007599130 0.0104473757 0.006679889 6.679889e-03 0.0167674625
[54,] 0.014113586 0.0244294299 0.020494978 1.908597e-02 0.0152479477
[55,] 0.025621988 0.0331344320 0.017224218 2.409977e-02 0.0453518074
[56,] 0.023718768 0.0341738981 0.034162440 3.936403e-02 0.0302477030
[57,] 0.035133170 0.0381381602 0.047611513 2.439543e-02 0.0476115132
[58,] 0.015111599 0.0160308402 0.036350037 1.894962e-02 0.0264470494
[59,] -0.014692823 -0.0021239571 -0.009754801 -5.791050e-03 -0.0020474488
[60,] -0.037345437 -0.0600818573 -0.045646170 -4.159362e-02 -0.0529906954
[61,] 0.026106414 0.0415552682 0.042547951 4.401238e-02 0.0326449634
[62,] 0.027313380 0.0440577540 0.033258240 3.784665e-02 0.0258163271
[,1] [,2] [,3]
[1,] 0.05157169 0.05353564 0.04533419
[2,] 0.03966726 0.05117484 0.04064836
[3,] 0.05389255 0.05812013 0.04952909
[4,] 0.05302011 0.04397099 0.04923552
[5,] 0.03716264 0.04819529 0.04306025
[6,] 0.04923552 0.05316247 0.03905337
[7,] 0.05558391 0.04105221 0.03870503
[8,] 0.04228085 0.02776720 0.02944016
[9,] 0.04891122 0.06296640 0.05142512
[10,] 0.04064836 0.04848887 0.04968312
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