VEC_110824
Luis Gerardo Vásquez Ramírez
2024-08-12
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library(highcharter)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(bvartools)## Cargando paquete requerido: coda## Cargando paquete requerido: Matrixlibrary(forecast)
library(vars)## Cargando paquete requerido: MASS## Cargando paquete requerido: strucchange## Cargando paquete requerido: zoo##
## Adjuntando el paquete: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Cargando paquete requerido: sandwich## Cargando paquete requerido: urca## Cargando paquete requerido: lmtest##
## Adjuntando el paquete: 'lmtest'## The following object is masked from 'package:highcharter':
##
## unemployment##
## Adjuntando el paquete: 'vars'## The following objects are masked from 'package:bvartools':
##
## fevd, irflibrary(tseries)
library(urca)
library(readxl)
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following object is masked from 'package:MASS':
##
## select## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(colorspace)# Configuración del directorio de trabajo
setwd("C:/Users/luisg/OneDrive/Escritorio/Tesis Cap 1 scrip")
df <- read_xlsx("BASE_VEC.xlsx", sheet = 2, col_names = TRUE)
# Estructura del dataframe
str(df)## tibble [342 × 4] (S3: tbl_df/tbl/data.frame)
## $ periodo : POSIXct[1:342], format: "1996-01-01" "1996-02-01" ...
## $ base_mon : num [1:342] 57 57 57 57 57 ...
## $ act_int_net: num [1:342] 57 57.9 58.5 57.8 58.6 ...
## $ cin : num [1:342] 57 56.9 56.9 56.9 56.9 ...colnames(df)## [1] "periodo" "base_mon" "act_int_net" "cin"summary(df)## periodo base_mon act_int_net
## Min. :1996-01-01 00:00:00.00 Min. : 56.99 Min. : 57.0
## 1st Qu.:2003-02-08 00:00:00.00 1st Qu.: 60.15 1st Qu.:164.4
## Median :2010-03-16 12:00:00.00 Median : 66.08 Median :309.4
## Mean :2010-03-17 06:31:34.74 Mean : 71.08 Mean :407.2
## 3rd Qu.:2017-04-23 12:00:00.00 3rd Qu.: 79.66 3rd Qu.:691.8
## Max. :2024-06-01 00:00:00.00 Max. :108.53 Max. :950.8
## cin
## Min. : 0.9193
## 1st Qu.:22.8496
## Median :42.4308
## Mean :37.6154
## 3rd Qu.:49.8259
## Max. :57.0000head(df)## # A tibble: 6 × 4
## periodo base_mon act_int_net cin
## <dttm> <dbl> <dbl> <dbl>
## 1 1996-01-01 00:00:00 57 57 57
## 2 1996-02-01 00:00:00 57.0 57.9 56.9
## 3 1996-03-01 00:00:00 57.0 58.5 56.9
## 4 1996-04-01 00:00:00 57.0 57.8 56.9
## 5 1996-05-01 00:00:00 57.0 58.6 56.9
## 6 1996-06-01 00:00:00 57.1 58.3 56.9tail(df)## # A tibble: 6 × 4
## periodo base_mon act_int_net cin
## <dttm> <dbl> <dbl> <dbl>
## 1 2024-01-01 00:00:00 107. 782. 39.9
## 2 2024-02-01 00:00:00 108. 770. 42.1
## 3 2024-03-01 00:00:00 109. 747. 44.9
## 4 2024-04-01 00:00:00 107. 777. 40.8
## 5 2024-05-01 00:00:00 108. 778. 40.8
## 6 2024-06-01 00:00:00 108. 841. 34.7valores <- df %>% dplyr::select(base_mon, act_int_net, cin)
plot(valores)
df_ts <- ts(df[c("act_int_net", "cin")], start = c(1996, 1), frequency = 12)
png("AIN_CIN.png", width = 800, height = 600)
plot(df_ts)
dev.off()## png
## 2plot(df_ts)
colnames(df_ts)## [1] "act_int_net" "cin"print(df_ts)## act_int_net cin
## Jan 1996 57.00000 57.0000000
## Feb 1996 57.87973 56.9102016
## Mar 1996 58.50433 56.8798450
## Apr 1996 57.80657 56.9051441
## May 1996 58.62242 56.8874379
## Jun 1996 58.31641 56.9382695
## Jul 1996 60.38088 56.7306504
## Aug 1996 61.34914 56.6248026
## Sep 1996 60.69349 56.7025293
## Oct 1996 62.83652 56.5373840
## Nov 1996 63.55730 56.5638294
## Dec 1996 64.74473 56.7233896
## Jan 1997 69.05787 56.1560341
## Feb 1997 71.91826 55.8569656
## Mar 1997 71.80941 55.9357797
## Apr 1997 74.84002 55.6038798
## May 1997 77.79477 55.3614595
## Jun 1997 77.60267 55.3740283
## Jul 1997 78.79332 55.3011371
## Aug 1997 80.55990 55.1195629
## Sep 1997 82.23247 54.9244216
## Oct 1997 85.91532 54.6308147
## Nov 1997 83.99663 54.9410137
## Dec 1997 86.48923 55.0312163
## Jan 1998 88.65374 54.6297387
## Feb 1998 88.60532 54.6130118
## Mar 1998 90.47672 54.3825645
## Apr 1998 91.94895 54.2939370
## May 1998 93.16251 54.2241942
## Jun 1998 93.36012 54.1629534
## Jul 1998 94.86885 54.0796985
## Aug 1998 96.27031 53.9146633
## Sep 1998 95.87470 53.9264749
## Oct 1998 98.19402 53.7919956
## Nov 1998 97.15113 53.9780258
## Dec 1998 100.29525 54.0863672
## Jan 1999 101.36669 53.7912517
## Feb 1999 101.24244 53.7501632
## Mar 1999 99.34353 54.0504938
## Apr 1999 98.82207 53.9811229
## May 1999 102.23268 53.7162464
## Jun 1999 101.79174 53.7559020
## Jul 1999 101.79673 53.8562330
## Aug 1999 103.70172 53.6333217
## Sep 1999 103.40036 53.6995461
## Oct 1999 104.64919 53.7006297
## Nov 1999 104.70363 53.8137497
## Dec 1999 105.57650 54.6459418
## Jan 2000 110.12469 53.5942809
## Feb 2000 110.21573 53.4517228
## Mar 2000 113.25828 53.2032543
## Apr 2000 111.52789 53.4876645
## May 2000 110.49312 53.5674351
## Jun 2000 111.44159 53.6219882
## Jul 2000 111.73985 53.5291857
## Aug 2000 113.87525 53.2309844
## Sep 2000 117.62482 52.9280482
## Oct 2000 120.50551 52.6682823
## Nov 2000 118.48372 53.1027829
## Dec 2000 121.26863 53.4675653
## Jan 2001 128.97337 52.1882563
## Feb 2001 128.54238 52.1234590
## Mar 2001 129.01123 52.0736731
## Apr 2001 127.29456 52.2153582
## May 2001 127.19017 52.2959543
## Jun 2001 126.66339 52.3951580
## Jul 2001 127.30992 52.2853629
## Aug 2001 128.38040 52.1906304
## Sep 2001 133.79345 51.7129310
## Oct 2001 132.68947 51.8077414
## Nov 2001 133.69669 51.9446859
## Dec 2001 134.40844 52.4751962
## Jan 2002 138.52854 51.7097978
## Feb 2002 138.10086 51.6651225
## Mar 2002 135.59348 52.1812630
## Apr 2002 136.74052 51.7820343
## May 2002 138.88404 51.7212242
## Jun 2002 142.60738 51.3992079
## Jul 2002 142.11264 51.4092186
## Aug 2002 144.03643 51.2260749
## Sep 2002 147.14244 50.9306671
## Oct 2002 145.42527 51.1575656
## Nov 2002 146.70878 51.2862701
## Dec 2002 156.93843 50.9601868
## Jan 2003 163.35123 49.9833092
## Feb 2003 165.52509 49.6789952
## Mar 2003 167.06556 49.4569265
## Apr 2003 164.28358 49.8454917
## May 2003 165.96807 49.7276216
## Jun 2003 166.05381 49.5987900
## Jul 2003 164.84062 49.7669508
## Aug 2003 169.84424 49.2673285
## Sep 2003 171.24951 49.0565875
## Oct 2003 174.57816 48.9002516
## Nov 2003 180.63822 48.5261341
## Dec 2003 182.51207 49.1672347
## Jan 2004 188.50379 48.0917284
## Feb 2004 189.20365 47.9308664
## Mar 2004 187.44672 47.9891865
## Apr 2004 187.40749 48.1006232
## May 2004 187.39054 48.1699030
## Jun 2004 188.96874 48.0544355
## Jul 2004 187.32470 48.2779313
## Aug 2004 188.65376 48.0533291
## Sep 2004 191.29138 47.8600646
## Oct 2004 190.20838 48.0519570
## Nov 2004 192.67549 47.9886419
## Dec 2004 192.52255 48.8645547
## Jan 2005 195.19145 48.1522936
## Feb 2005 191.58302 48.3330366
## Mar 2005 192.68786 48.3106146
## Apr 2005 188.74317 48.6110438
## May 2005 184.93240 49.0036380
## Jun 2005 190.82092 48.5399916
## Jul 2005 190.47718 48.5964690
## Aug 2005 196.53407 47.8172093
## Sep 2005 200.01834 47.5925881
## Oct 2005 202.44140 47.4271733
## Nov 2005 202.85682 47.5888875
## Dec 2005 206.24721 48.2545983
## Jan 2006 208.94407 47.5061184
## Feb 2006 206.80739 47.5611365
## Mar 2006 213.91870 46.9786748
## Apr 2006 221.91046 46.2733368
## May 2006 231.49729 45.3231835
## Jun 2006 238.60669 44.8250964
## Jul 2006 228.94742 45.7792787
## Aug 2006 230.00048 45.5748250
## Sep 2006 230.88433 45.5572062
## Oct 2006 215.25070 47.1350392
## Nov 2006 217.64246 47.4169178
## Dec 2006 213.26234 48.8806250
## Jan 2007 217.57055 47.6950256
## Feb 2007 219.29713 47.3268291
## Mar 2007 215.58485 47.8901669
## Apr 2007 217.66282 47.5093104
## May 2007 215.85627 47.8140799
## Jun 2007 216.36814 47.7915572
## Jul 2007 219.04705 47.5142332
## Aug 2007 223.31061 47.1401802
## Sep 2007 227.27708 46.7760316
## Oct 2007 226.87773 46.9024493
## Nov 2007 233.73291 46.5019224
## Dec 2007 237.52187 47.3180367
## Jan 2008 243.39794 46.0678052
## Feb 2008 240.77016 46.1043163
## Mar 2008 241.05221 46.0740204
## Apr 2008 241.97825 45.9399301
## May 2008 241.09774 46.1378555
## Jun 2008 240.84105 46.0932030
## Jul 2008 238.68352 46.6057244
## Aug 2008 246.33953 45.8339419
## Sep 2008 263.00803 44.0951975
## Oct 2008 261.97080 44.5606753
## Nov 2008 281.36763 43.1493028
## Dec 2008 307.11375 41.9612951
## Jan 2009 302.55984 41.9448814
## Feb 2009 314.94028 40.4550875
## Mar 2009 287.10877 43.1230833
## Apr 2009 278.29071 44.0676135
## May 2009 264.82014 45.3948867
## Jun 2009 260.67321 45.7132783
## Jul 2009 263.15111 45.6455850
## Aug 2009 270.92411 44.6522595
## Sep 2009 281.90499 43.4697079
## Oct 2009 278.12032 44.0312239
## Nov 2009 279.35070 44.1579585
## Dec 2009 304.74993 43.2307073
## Jan 2010 300.79561 43.0244887
## Feb 2010 299.58843 42.8362666
## Mar 2010 294.91214 43.5489471
## Apr 2010 298.73402 42.7351861
## May 2010 311.78501 41.5926906
## Jun 2010 314.54619 41.2265569
## Jul 2010 320.17681 40.9576419
## Aug 2010 336.09053 39.2013774
## Sep 2010 329.13998 39.9909496
## Oct 2010 329.54394 39.9652823
## Nov 2010 335.10479 39.7088793
## Dec 2010 339.96344 40.8901536
## Jan 2011 342.22311 39.8037617
## Feb 2011 347.90024 39.0535625
## Mar 2011 347.24012 39.0515452
## Apr 2011 344.52356 39.5282843
## May 2011 345.59047 39.2705281
## Jun 2011 355.28907 38.2618534
## Jul 2011 366.08910 37.3827675
## Aug 2011 393.64724 34.4427653
## Sep 2011 427.22881 31.2509242
## Oct 2011 422.27655 31.9006407
## Nov 2011 437.96048 30.7942141
## Dec 2011 452.80729 30.9859622
## Jan 2012 438.23358 31.7005196
## Feb 2012 433.96174 31.8743472
## Mar 2012 436.88225 31.8190065
## Apr 2012 450.78102 30.5946718
## May 2012 488.60029 27.0330154
## Jun 2012 472.00288 28.7965218
## Jul 2012 474.21638 28.5458367
## Aug 2012 473.97850 28.3959039
## Sep 2012 462.32422 29.3759963
## Oct 2012 473.81254 28.4532752
## Nov 2012 470.77883 29.1043485
## Dec 2012 468.96857 30.9432360
## Jan 2013 470.98671 29.6241403
## Feb 2013 474.46998 29.1116284
## Mar 2013 459.72376 31.0563541
## Apr 2013 455.92829 30.6968323
## May 2013 471.23932 29.3162471
## Jun 2013 475.54659 28.9072886
## Jul 2013 479.27980 28.4647509
## Aug 2013 497.39554 26.6562600
## Sep 2013 495.59625 26.8465981
## Oct 2013 495.34494 27.0642924
## Nov 2013 503.90936 27.0000664
## Dec 2013 505.54325 28.6657848
## Jan 2014 522.74715 26.0282653
## Feb 2014 519.81631 26.2297395
## Mar 2014 517.64061 26.3258764
## Apr 2014 527.30447 25.6506521
## May 2014 523.35461 26.0896674
## Jun 2014 532.16871 25.2174970
## Jul 2014 542.44645 24.4092129
## Aug 2014 538.10875 24.8243259
## Sep 2014 551.15012 23.4663090
## Oct 2014 562.27620 22.8420302
## Nov 2014 577.21322 22.0444380
## Dec 2014 605.98197 21.4189029
## Jan 2015 625.14335 18.7870045
## Feb 2015 621.20578 19.1924674
## Mar 2015 631.56790 18.8979516
## Apr 2015 637.21842 17.9958932
## May 2015 632.13552 18.7205657
## Jun 2015 637.08752 18.1609019
## Jul 2015 648.63552 17.3728403
## Aug 2015 654.90074 16.8670465
## Sep 2015 642.39714 17.9899115
## Oct 2015 614.05402 21.2361672
## Nov 2015 602.06327 22.8722160
## Dec 2015 640.06096 21.4193014
## Jan 2016 667.63016 17.9546757
## Feb 2016 671.76048 17.2953566
## Mar 2016 646.60033 20.2493699
## Apr 2016 652.42760 19.4122930
## May 2016 685.60363 16.5362701
## Jun 2016 685.57585 16.8055532
## Jul 2016 699.39965 15.7282725
## Aug 2016 695.52118 15.9739893
## Sep 2016 722.87047 13.3761918
## Oct 2016 692.91721 16.6394477
## Nov 2016 744.08615 12.3042703
## Dec 2016 755.87891 13.2783773
## Jan 2017 756.39411 12.5158520
## Feb 2017 741.27529 13.7376342
## Mar 2017 696.39929 18.2471325
## Apr 2017 694.91469 18.5627685
## May 2017 683.61821 19.6171793
## Jun 2017 660.03235 21.8892845
## Jul 2017 653.02011 22.5761573
## Aug 2017 649.39660 22.6306288
## Sep 2017 661.50644 21.4793542
## Oct 2017 700.13878 17.9129875
## Nov 2017 673.71097 21.2306049
## Dec 2017 713.74906 19.8573641
## Jan 2018 692.12145 20.8187750
## Feb 2018 693.77449 20.5714091
## Mar 2018 674.67526 23.2955957
## Apr 2018 690.78411 21.1948301
## May 2018 735.50705 17.0135197
## Jun 2018 724.29902 18.5870903
## Jul 2018 683.86971 22.5050613
## Aug 2018 701.42531 20.5932656
## Sep 2018 687.98406 21.7264525
## Oct 2018 740.03384 16.9139288
## Nov 2018 740.95303 17.5380578
## Dec 2018 716.85318 21.9827247
## Jan 2019 710.64137 21.2626960
## Feb 2019 720.71307 19.7924587
## Mar 2019 728.67067 18.6599758
## Apr 2019 725.26902 19.1988344
## May 2019 750.40362 16.7510931
## Jun 2019 737.95568 17.9915587
## Jul 2019 736.59608 17.8721230
## Aug 2019 780.55441 13.6504357
## Sep 2019 744.22845 17.0180743
## Oct 2019 728.33993 18.7267638
## Nov 2019 746.13352 18.0654539
## Dec 2019 714.28202 23.5341768
## Jan 2020 737.91121 20.3318543
## Feb 2020 766.38755 16.9780315
## Mar 2020 905.75924 5.0044503
## Apr 2020 950.84755 0.9192558
## May 2020 890.68773 8.0286866
## Jun 2020 923.32142 4.8988663
## Jul 2020 893.51047 8.9591734
## Aug 2020 884.81642 9.8299243
## Sep 2020 899.45810 8.0564737
## Oct 2020 861.00789 12.3046347
## Nov 2020 828.78041 16.8425710
## Dec 2020 811.43732 20.9932781
## Jan 2021 839.35346 17.9004661
## Feb 2021 855.17627 16.0689913
## Mar 2021 832.55519 20.0286221
## Apr 2021 832.75359 19.4434941
## May 2021 815.94906 21.0941405
## Jun 2021 812.86810 21.5682739
## Jul 2021 814.37365 21.8308220
## Aug 2021 865.44001 16.6789420
## Sep 2021 886.97791 15.0042681
## Oct 2021 884.04710 15.8016609
## Nov 2021 925.75952 12.8108765
## Dec 2021 866.52110 21.6305339
## Jan 2022 880.97793 20.0562497
## Feb 2022 873.67646 20.8757857
## Mar 2022 851.44155 24.4786008
## Apr 2022 860.26719 22.8914498
## May 2022 829.47646 25.7569230
## Jun 2022 837.28627 24.8138560
## Jul 2022 845.43618 24.6512688
## Aug 2022 841.64633 24.4713874
## Sep 2022 830.01395 26.0274986
## Oct 2022 816.26863 27.6182591
## Nov 2022 803.64711 29.8910608
## Dec 2022 802.32222 32.9418715
## Jan 2023 794.86913 32.9864139
## Feb 2023 771.64678 34.8614595
## Mar 2023 765.22397 36.2766579
## Apr 2023 768.21808 35.6334469
## May 2023 757.46576 37.1023331
## Jun 2023 743.04058 38.5563427
## Jul 2023 729.23924 40.4285378
## Aug 2023 734.25823 39.3433757
## Sep 2023 751.43206 38.0607683
## Oct 2023 774.09661 36.1021338
## Nov 2023 753.94228 39.2761113
## Dec 2023 747.17050 43.3744686
## Jan 2024 781.83431 39.8696616
## Feb 2024 769.90480 42.1264897
## Mar 2024 747.45627 44.9186030
## Apr 2024 777.26696 40.7562588
## May 2024 778.48315 40.8139565
## Jun 2024 841.33920 34.6593856#PRUEBA DE ESTACIONARIEDAD
#R>ain
#DP>cin#ain = act_int_net
#cin = cin
adf.ain<- ur.df(df_ts[,1], type = "trend", selectlags = "BIC")
summary(adf.ain) ##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.441 -5.730 -0.738 3.824 137.834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.337330 1.992808 -0.169 0.8657
## z.lag.1 -0.033571 0.013330 -2.518 0.0123 *
## tt 0.095044 0.037201 2.555 0.0111 *
## z.diff.lag 0.001445 0.055443 0.026 0.9792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.82 on 336 degrees of freedom
## Multiple R-squared: 0.01934, Adjusted R-squared: 0.01059
## F-statistic: 2.209 on 3 and 336 DF, p-value: 0.08687
##
##
## Value of test-statistic is: -2.5184 4.3346 3.2796
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.98 -3.42 -3.13
## phi2 6.15 4.71 4.05
## phi3 8.34 6.30 5.36png("residuales_ain.png", width = 800, height = 600)
plot(adf.ain)
dev.off()## png
## 2adf.test(df_ts[,1])##
## Augmented Dickey-Fuller Test
##
## data: df_ts[, 1]
## Dickey-Fuller = -2.2063, Lag order = 6, p-value = 0.4897
## alternative hypothesis: stationarypp.test(df_ts[,1])##
## Phillips-Perron Unit Root Test
##
## data: df_ts[, 1]
## Dickey-Fuller Z(alpha) = -10.807, Truncation lag parameter = 5, p-value
## = 0.5049
## alternative hypothesis: stationaryadf.cin <- ur.df(df_ts[,2], type = "trend", selectlags = "BIC")
summary(adf.cin)##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.2981 -0.5910 0.0505 0.4751 8.4693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.829975 0.825767 1.005 0.316
## z.lag.1 -0.017780 0.013459 -1.321 0.187
## tt -0.001342 0.001991 -0.674 0.501
## z.diff.lag -0.055323 0.056311 -0.982 0.327
##
## Residual standard error: 1.795 on 336 degrees of freedom
## Multiple R-squared: 0.01196, Adjusted R-squared: 0.00314
## F-statistic: 1.356 on 3 and 336 DF, p-value: 0.2562
##
##
## Value of test-statistic is: -1.3211 1.0442 1.3181
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.98 -3.42 -3.13
## phi2 6.15 4.71 4.05
## phi3 8.34 6.30 5.36adf.test(df_ts[,2])##
## Augmented Dickey-Fuller Test
##
## data: df_ts[, 2]
## Dickey-Fuller = -0.57528, Lag order = 6, p-value = 0.9781
## alternative hypothesis: stationarypng("residuales_cin.png", width = 800, height = 600)
plot(adf.cin)
dev.off()## png
## 2pp.test(df_ts[,2])##
## Phillips-Perron Unit Root Test
##
## data: df_ts[, 2]
## Dickey-Fuller Z(alpha) = -5.4415, Truncation lag parameter = 5, p-value
## = 0.8055
## alternative hypothesis: stationarydiff.adf.ain <- ur.df(diff(df_ts[,1]), type = "trend", selectlags = "BIC")
summary(diff.adf.ain) ##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61.712 -4.722 -0.809 3.759 137.609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.668205 1.864624 0.895 0.372
## z.lag.1 -1.045744 0.079256 -13.195 <2e-16 ***
## tt 0.004317 0.009433 0.458 0.647
## z.diff.lag 0.030811 0.055660 0.554 0.580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17 on 335 degrees of freedom
## Multiple R-squared: 0.4983, Adjusted R-squared: 0.4938
## F-statistic: 110.9 on 3 and 335 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -13.1945 58.0793 87.0991
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.98 -3.42 -3.13
## phi2 6.15 4.71 4.05
## phi3 8.34 6.30 5.36png("residuales_ain_dif.png", width = 800, height = 600)
plot(diff.adf.ain)
dev.off()## png
## 2diff.adf.cin <- ur.df(diff(df_ts[,2]), type = "trend", selectlags = "BIC")
summary(diff.adf.cin) ##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.4085 -0.5749 0.0559 0.4713 8.5699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.242976 0.197880 -1.228 0.220
## z.lag.1 -1.117582 0.081332 -13.741 <2e-16 ***
## tt 0.001005 0.001004 1.001 0.317
## z.diff.lag 0.044980 0.055598 0.809 0.419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.8 on 335 degrees of freedom
## Multiple R-squared: 0.5266, Adjusted R-squared: 0.5223
## F-statistic: 124.2 on 3 and 335 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -13.7409 62.9821 94.4565
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.98 -3.42 -3.13
## phi2 6.15 4.71 4.05
## phi3 8.34 6.30 5.36png("residuales_cin_dif.png", width = 800, height = 600)
plot(diff.adf.cin)
dev.off()## png
## 2acf(df_ts[,2])
acf(df_ts[,1])
adf.test(diff(df_ts[,1]))## Warning in adf.test(diff(df_ts[, 1])): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: diff(df_ts[, 1])
## Dickey-Fuller = -7.4897, Lag order = 6, p-value = 0.01
## alternative hypothesis: stationaryadf.test(diff(df_ts[,2]))## Warning in adf.test(diff(df_ts[, 2])): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: diff(df_ts[, 2])
## Dickey-Fuller = -7.1785, Lag order = 6, p-value = 0.01
## alternative hypothesis: stationary#Una vez las series son estacionarias, se procede a realiza la estimacion
var_aic <- VARselect(df_ts,lag.max=10, type = "both")
VARselect(df_ts)## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 9 1 1 9
##
## $criteria
## 1 2 3 4 5 6
## AIC(n) 4.732936 4.737250 4.723125 4.740606 4.678385 4.682729
## HQ(n) 4.760360 4.782957 4.787115 4.822879 4.778942 4.801568
## SC(n) 4.801704 4.851862 4.883582 4.946909 4.930533 4.980722
## FPE(n) 113.628804 114.120421 112.520694 114.506626 107.601420 108.073250
## 7 8 9 10
## AIC(n) 4.666599 4.675922 4.646450 4.653435
## HQ(n) 4.803721 4.831327 4.820138 4.845406
## SC(n) 5.010437 5.065605 5.081978 5.134807
## FPE(n) 106.348598 107.350752 104.240483 104.980328var_aic## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 9 1 1 9
##
## $criteria
## 1 2 3 4 5 6
## AIC(n) 4.722757 4.725431 4.711860 4.730710 4.668572 4.670862
## HQ(n) 4.759323 4.780279 4.784992 4.822124 4.778270 4.798843
## SC(n) 4.814447 4.862966 4.895240 4.959935 4.943642 4.991777
## FPE(n) 112.478233 112.779931 111.261014 113.380108 106.552206 106.800488
## 7 8 9 10
## AIC(n) 4.652956 4.665148 4.631101 4.637564
## HQ(n) 4.799220 4.829694 4.813931 4.838677
## SC(n) 5.019716 5.077753 5.089551 5.141859
## FPE(n) 104.910320 106.203862 102.656976 103.332641#SI USAMOS 1 REZAGO NOS ENFOCAMOS EN CORTO PLAZO
p1ctCcp <- VAR(df_ts, p = 1, type = "both")
p1ctCcp##
## VAR Estimation Results:
## =======================
##
## Estimated coefficients for equation act_int_net:
## ================================================
## Call:
## act_int_net = act_int_net.l1 + cin.l1 + const + trend
##
## act_int_net.l1 cin.l1 const trend
## 0.9583741 -0.1144743 5.9498751 0.1020572
##
##
## Estimated coefficients for equation cin:
## ========================================
## Call:
## cin = act_int_net.l1 + cin.l1 + const + trend
##
## act_int_net.l1 cin.l1 const trend
## 0.00487331 1.01083420 -0.57376472 -0.01091586#SI USAMOS 9 REZAGO NOS ENFOCAMOS EN LARGO PLAZO
p1ct <- VAR(df_ts, p = 9, type = "both")
p1ct##
## VAR Estimation Results:
## =======================
##
## Estimated coefficients for equation act_int_net:
## ================================================
## Call:
## act_int_net = act_int_net.l1 + cin.l1 + act_int_net.l2 + cin.l2 + act_int_net.l3 + cin.l3 + act_int_net.l4 + cin.l4 + act_int_net.l5 + cin.l5 + act_int_net.l6 + cin.l6 + act_int_net.l7 + cin.l7 + act_int_net.l8 + cin.l8 + act_int_net.l9 + cin.l9 + const + trend
##
## act_int_net.l1 cin.l1 act_int_net.l2 cin.l2 act_int_net.l3
## 1.10467196 1.00680789 -0.24833570 -1.63360847 -0.01834409
## cin.l3 act_int_net.l4 cin.l4 act_int_net.l5 cin.l5
## -0.89383967 0.04972487 2.58659754 0.26370768 -0.62599779
## act_int_net.l6 cin.l6 act_int_net.l7 cin.l7 act_int_net.l8
## -0.23926824 0.02284574 -0.23913372 -3.40174775 0.34222311
## cin.l8 act_int_net.l9 cin.l9 const trend
## 4.13188424 -0.06438929 -1.49328166 16.98466332 0.09824562
##
##
## Estimated coefficients for equation cin:
## ========================================
## Call:
## cin = act_int_net.l1 + cin.l1 + act_int_net.l2 + cin.l2 + act_int_net.l3 + cin.l3 + act_int_net.l4 + cin.l4 + act_int_net.l5 + cin.l5 + act_int_net.l6 + cin.l6 + act_int_net.l7 + cin.l7 + act_int_net.l8 + cin.l8 + act_int_net.l9 + cin.l9 + const + trend
##
## act_int_net.l1 cin.l1 act_int_net.l2 cin.l2 act_int_net.l3
## -0.02292881 0.74372183 0.01126177 0.02209318 0.02297041
## cin.l3 act_int_net.l4 cin.l4 act_int_net.l5 cin.l5
## 0.30427220 -0.02167284 -0.46344765 -0.01286079 0.25920581
## act_int_net.l6 cin.l6 act_int_net.l7 cin.l7 act_int_net.l8
## 0.01215320 -0.13304440 0.04130381 0.51830187 -0.04621288
## cin.l8 act_int_net.l9 cin.l9 const trend
## -0.55779646 0.02418871 0.36284119 -3.24500856 -0.01211276summary(p1ct, equation = "act_int_net")##
## VAR Estimation Results:
## =========================
## Endogenous variables: act_int_net, cin
## Deterministic variables: both
## Sample size: 333
## Log Likelihood: -1675.012
## Roots of the characteristic polynomial:
## 1.007 0.9873 0.8786 0.8786 0.8576 0.8576 0.8289 0.8289 0.8012 0.8012 0.7717 0.7717 0.7715 0.7715 0.741 0.741 0.5132 0.5132
## Call:
## VAR(y = df_ts, p = 9, type = "both")
##
##
## Estimation results for equation act_int_net:
## ============================================
## act_int_net = act_int_net.l1 + cin.l1 + act_int_net.l2 + cin.l2 + act_int_net.l3 + cin.l3 + act_int_net.l4 + cin.l4 + act_int_net.l5 + cin.l5 + act_int_net.l6 + cin.l6 + act_int_net.l7 + cin.l7 + act_int_net.l8 + cin.l8 + act_int_net.l9 + cin.l9 + const + trend
##
## Estimate Std. Error t value Pr(>|t|)
## act_int_net.l1 1.10467 0.16746 6.597 1.79e-10 ***
## cin.l1 1.00681 1.60668 0.627 0.5314
## act_int_net.l2 -0.24834 0.22669 -1.095 0.2742
## cin.l2 -1.63361 2.10431 -0.776 0.4381
## act_int_net.l3 -0.01834 0.22722 -0.081 0.9357
## cin.l3 -0.89384 2.10367 -0.425 0.6712
## act_int_net.l4 0.04972 0.22859 0.218 0.8279
## cin.l4 2.58660 2.13043 1.214 0.2256
## act_int_net.l5 0.26371 0.22834 1.155 0.2490
## cin.l5 -0.62600 2.11999 -0.295 0.7680
## act_int_net.l6 -0.23927 0.22859 -1.047 0.2960
## cin.l6 0.02285 2.12451 0.011 0.9914
## act_int_net.l7 -0.23913 0.23135 -1.034 0.3021
## cin.l7 -3.40175 2.15443 -1.579 0.1154
## act_int_net.l8 0.34222 0.23434 1.460 0.1452
## cin.l8 4.13188 2.18511 1.891 0.0596 .
## act_int_net.l9 -0.06439 0.17496 -0.368 0.7131
## cin.l9 -1.49328 1.69947 -0.879 0.3803
## const 16.98466 12.85538 1.321 0.1874
## trend 0.09825 0.04182 2.349 0.0194 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 16.66 on 313 degrees of freedom
## Multiple R-Squared: 0.9965, Adjusted R-squared: 0.9963
## F-statistic: 4674 on 19 and 313 DF, p-value: < 2.2e-16
##
##
##
## Covariance matrix of residuals:
## act_int_net cin
## act_int_net 277.49 -27.072
## cin -27.07 2.968
##
## Correlation matrix of residuals:
## act_int_net cin
## act_int_net 1.0000 -0.9433
## cin -0.9433 1.0000#LOS RESULTADOS MUESTRAN QUE EL AJUSTE ES BUENO PERO PUEDE ESTAR SOBREESTIMADO
png("adf_plot5.png", width = 800, height = 600)
plot(p1ct, names = "act_int_net")
dev.off()## png
## 2summary(p1ct, equation = "cin")##
## VAR Estimation Results:
## =========================
## Endogenous variables: act_int_net, cin
## Deterministic variables: both
## Sample size: 333
## Log Likelihood: -1675.012
## Roots of the characteristic polynomial:
## 1.007 0.9873 0.8786 0.8786 0.8576 0.8576 0.8289 0.8289 0.8012 0.8012 0.7717 0.7717 0.7715 0.7715 0.741 0.741 0.5132 0.5132
## Call:
## VAR(y = df_ts, p = 9, type = "both")
##
##
## Estimation results for equation cin:
## ====================================
## cin = act_int_net.l1 + cin.l1 + act_int_net.l2 + cin.l2 + act_int_net.l3 + cin.l3 + act_int_net.l4 + cin.l4 + act_int_net.l5 + cin.l5 + act_int_net.l6 + cin.l6 + act_int_net.l7 + cin.l7 + act_int_net.l8 + cin.l8 + act_int_net.l9 + cin.l9 + const + trend
##
## Estimate Std. Error t value Pr(>|t|)
## act_int_net.l1 -0.022929 0.017320 -1.324 0.18653
## cin.l1 0.743722 0.166174 4.476 1.07e-05 ***
## act_int_net.l2 0.011262 0.023446 0.480 0.63134
## cin.l2 0.022093 0.217643 0.102 0.91921
## act_int_net.l3 0.022970 0.023501 0.977 0.32912
## cin.l3 0.304272 0.217577 1.398 0.16297
## act_int_net.l4 -0.021673 0.023642 -0.917 0.36000
## cin.l4 -0.463448 0.220345 -2.103 0.03624 *
## act_int_net.l5 -0.012861 0.023616 -0.545 0.58643
## cin.l5 0.259206 0.219265 1.182 0.23804
## act_int_net.l6 0.012153 0.023642 0.514 0.60758
## cin.l6 -0.133044 0.219733 -0.605 0.54530
## act_int_net.l7 0.041304 0.023928 1.726 0.08531 .
## cin.l7 0.518302 0.222827 2.326 0.02066 *
## act_int_net.l8 -0.046213 0.024237 -1.907 0.05748 .
## cin.l8 -0.557796 0.226000 -2.468 0.01412 *
## act_int_net.l9 0.024189 0.018095 1.337 0.18228
## cin.l9 0.362841 0.175772 2.064 0.03982 *
## const -3.245009 1.329598 -2.441 0.01522 *
## trend -0.012113 0.004325 -2.801 0.00542 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 1.723 on 313 degrees of freedom
## Multiple R-Squared: 0.9864, Adjusted R-squared: 0.9856
## F-statistic: 1198 on 19 and 313 DF, p-value: < 2.2e-16
##
##
##
## Covariance matrix of residuals:
## act_int_net cin
## act_int_net 277.49 -27.072
## cin -27.07 2.968
##
## Correlation matrix of residuals:
## act_int_net cin
## act_int_net 1.0000 -0.9433
## cin -0.9433 1.0000png("adf_plot6.png", width = 800, height = 600)
plot(p1ct , name="cin")
dev.off()## png
## 2names(p1ct$varresult)## [1] "act_int_net" "cin"ser11 <- serial.test(p1ct, lags.pt = 10, type = "PT.asymptotic")
ser11$serial##
## Portmanteau Test (asymptotic)
##
## data: Residuals of VAR object p1ct
## Chi-squared = 33.732, df = 4, p-value = 8.456e-07norm1 <-normality.test(p1ct)
norm1$jb.mul## $JB
##
## JB-Test (multivariate)
##
## data: Residuals of VAR object p1ct
## Chi-squared = 5319.5, df = 4, p-value < 2.2e-16
##
##
## $Skewness
##
## Skewness only (multivariate)
##
## data: Residuals of VAR object p1ct
## Chi-squared = 387.37, df = 2, p-value < 2.2e-16
##
##
## $Kurtosis
##
## Kurtosis only (multivariate)
##
## data: Residuals of VAR object p1ct
## Chi-squared = 4932.2, df = 2, p-value < 2.2e-16arch1 <- arch.test(p1ct, lags.multi = 9)
arch1$arch.mul##
## ARCH (multivariate)
##
## data: Residuals of VAR object p1ct
## Chi-squared = 210.06, df = 81, p-value = 2.091e-13plot(arch1, names = "act_int_net")
plot(stability(p1ct), nc = 2)
vec <- ca.jo(df_ts, ecdet = "none", type = "trace",
K = 4, spec = "transitory", season = 4)summary(vec)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 5.622967e-02 2.834612e-05
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.01 6.50 8.18 11.65
## r = 0 | 19.57 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l1 cin.l1
## act_int_net.l1 1.000000 1.00000
## cin.l1 5.526244 21.25998
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l1 cin.l1
## act_int_net.d 0.0006946586 7.931634e-04
## cin.d 0.0006741948 -7.984191e-05class(vec)## [1] "ca.jo"
## attr(,"package")
## [1] "urca"var <- vec2var(vec, r = 1)
var##
## Coefficient matrix of lagged endogenous variables:
##
## A1:
## act_int_net.l1 cin.l1
## act_int_net 1.06838034 0.8261167
## cin -0.01258539 0.8074789
##
##
## A2:
## act_int_net.l2 cin.l2
## act_int_net -0.28112513 -2.7472436
## cin 0.03126265 0.3314435
##
##
## A3:
## act_int_net.l3 cin.l3
## act_int_net 0.123704835 0.932865766
## cin -0.004561111 0.009600664
##
##
## A4:
## act_int_net.l4 cin.l4
## act_int_net 0.08973462 0.9921000
## cin -0.01344195 -0.1447972
##
##
## Coefficient matrix of deterministic regressor(s).
##
## constant sd1 sd2 sd3
## act_int_net 2.2932971 -0.6346339 -2.1840369 -4.1586653
## cin -0.5148073 -0.5282285 -0.2761127 0.2232081class(var)## [1] "vec2var"var1<-cajorls(vec, r=1)
var1## $rlm
##
## Call:
## lm(formula = substitute(form1), data = data.mat)
##
## Coefficients:
## act_int_net.d cin.d
## ect1 0.0006947 0.0006742
## constant 2.2932971 -0.5148073
## sd1 -0.6346339 -0.5282285
## sd2 -2.1840369 -0.2761127
## sd3 -4.1586653 0.2232081
## act_int_net.dl1 0.0676857 -0.0132596
## cin.dl1 0.8222778 -0.1962469
## act_int_net.dl2 -0.2134395 0.0180031
## cin.dl2 -1.9249658 0.1351966
## act_int_net.dl3 -0.0897346 0.0134420
## cin.dl3 -0.9921000 0.1447972
##
##
## $beta
## ect1
## act_int_net.l1 1.000000
## cin.l1 5.526244ser11 <- serial.test(var, lags.pt = 9, type = "PT.asymptotic")
ser11$serial##
## Portmanteau Test (asymptotic)
##
## data: Residuals of VAR object var
## Chi-squared = 107.68, df = 22, p-value = 2.855e-13norm1 <-normality.test(var)
norm1$jb.mul## $JB
##
## JB-Test (multivariate)
##
## data: Residuals of VAR object var
## Chi-squared = 4187.3, df = 4, p-value < 2.2e-16
##
##
## $Skewness
##
## Skewness only (multivariate)
##
## data: Residuals of VAR object var
## Chi-squared = 296.66, df = 2, p-value < 2.2e-16
##
##
## $Kurtosis
##
## Kurtosis only (multivariate)
##
## data: Residuals of VAR object var
## Chi-squared = 3890.7, df = 2, p-value < 2.2e-16arch1 <- arch.test(var, lags.multi = 9)
arch1$arch.mul##
## ARCH (multivariate)
##
## data: Residuals of VAR object var
## Chi-squared = 195.65, df = 81, p-value = 1.77e-11plot(arch1, names = "act_int_net")## Warning in plot.varcheck(arch1, names = "act_int_net"):
## Invalid residual name(s) supplied, using residuals of first variable.
#plot(vars::stability(var), nc = 2)
ir <- vars::irf(var, n.ahead = 20, impulse = "act_int_net", response = "cin")
plot(ir)
ir1 <- vars::irf(var, n.ahead = 20, impulse = "cin", response = "act_int_net")
plot(ir1)
fevd.R <- vars::fevd(var, n.ahead =9)
plot(fevd.R)
predictions <- predict(var, n.ahead = 9, ci = 0.95)
plot(predictions)
rt=df_ts[,1] -3.9619*df_ts[,2]
plot(rt)
adf.test(rt)##
## Augmented Dickey-Fuller Test
##
## data: rt
## Dickey-Fuller = -1.9946, Lag order = 6, p-value = 0.579
## alternative hypothesis: stationarypp.test(rt)##
## Phillips-Perron Unit Root Test
##
## data: rt
## Dickey-Fuller Z(alpha) = -10.943, Truncation lag parameter = 5, p-value
## = 0.4973
## alternative hypothesis: stationarysummary(ur.df(rt))##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression none
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -95.105 -4.201 1.218 7.794 186.048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## z.lag.1 0.002422 0.003113 0.778 0.437
## z.diff.lag -0.022306 0.055622 -0.401 0.689
##
## Residual standard error: 23.87 on 338 degrees of freedom
## Multiple R-squared: 0.002126, Adjusted R-squared: -0.003779
## F-statistic: 0.36 on 2 and 338 DF, p-value: 0.6979
##
##
## Value of test-statistic is: 0.7779
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau1 -2.58 -1.95 -1.62Normalizacion de los errores
# Instalar y cargar el paquete 'rugarch' si aún no lo has hecho
library(rugarch)## Cargando paquete requerido: parallel##
## Adjuntando el paquete: 'rugarch'## The following object is masked from 'package:stats':
##
## sigma# Extraer residuos del modelo VAR
residuos <- residuals(var)
# Ajustar un modelo GARCH(1,1) para los residuos
garch_spec <- ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),
mean.model = list(armaOrder = c(0, 0)),
distribution.model = "norm")
# Ajustar el modelo a los residuos
garch_fit <- ugarchfit(spec = garch_spec, data = residuos[,1])
summary(garch_fit)## Length Class Mode
## 1 uGARCHfit S4# Para la variable 'cin', ajustar otro modelo GARCH si es necesario
garch_fit_cin <- ugarchfit(spec = garch_spec, data = residuos[,2])
summary(garch_fit_cin)## Length Class Mode
## 1 uGARCHfit S4# Diagnósticos del modelo GARCH para 'act_int_net'
plot(garch_fit, which = "all")##
## please wait...calculating quantiles...
#Diagnósticos del modelo GARCH para 'cin'
plot(garch_fit_cin, which = "all")##
## please wait...calculating quantiles...
# Predicciones para 'act_int_net'
forecast_garch_fit <- ugarchforecast(garch_fit, n.ahead = 10)
plot(forecast_garch_fit, which = 1)
# Predicciones para 'cin'
forecast_garch_fit_cin <- ugarchforecast(garch_fit_cin, n.ahead = 10)
plot(forecast_garch_fit_cin, which = 1)
# Guardar el resumen del modelo GARCH para 'act_int_net'
sink("garch_fit_act_int_net_summary.txt")
print(summary(garch_fit))## Length Class Mode
## 1 uGARCHfit S4sink()
# Guardar el resumen del modelo GARCH para 'cin'
sink("garch_fit_cin_summary.txt")
print(summary(garch_fit_cin))## Length Class Mode
## 1 uGARCHfit S4sink()# Guardar gráficos
png("garch_fit_act_int_net_plot.png", width = 800, height = 600)
plot(garch_fit, which = "all")##
## please wait...calculating quantiles...dev.off()## png
## 2png("garch_fit_cin_plot.png", width = 800, height = 600)
plot(garch_fit_cin, which = "all")##
## please wait...calculating quantiles...dev.off()## png
## 2summary(garch_fit)## Length Class Mode
## 1 uGARCHfit S4summary(garch_fit_cin)## Length Class Mode
## 1 uGARCHfit S4#Solo nos arroja la clasificacion de los datos# Parámetros del modelo para act_int_net
coef(garch_fit)## mu omega alpha1 beta1
## -1.243529e-11 1.358603e+00 2.768494e-01 7.221497e-01# Parámetros del modelo para cin
coef(garch_fit_cin)## mu omega alpha1 beta1
## 1.140483e-13 1.581597e-02 2.372203e-01 7.607179e-01# Para act_int_net
info_garch_fit <- infocriteria(garch_fit)
cat("Log-Likelihood para act_int_net:", info_garch_fit["LogLikelihood"], "\n")## Log-Likelihood para act_int_net: NAcat("AIC para act_int_net:", info_garch_fit["Akaike"], "\n")## AIC para act_int_net: NAcat("BIC para act_int_net:", info_garch_fit["Bayesian"], "\n")## BIC para act_int_net: NA# Para cin
info_garch_fit_cin <- infocriteria(garch_fit_cin)
cat("Log-Likelihood para cin:", info_garch_fit_cin["LogLikelihood"], "\n")## Log-Likelihood para cin: NAcat("AIC para cin:", info_garch_fit_cin["Akaike"], "\n")## AIC para cin: NAcat("BIC para cin:", info_garch_fit_cin["Bayesian"], "\n")## BIC para cin: NA# Log-Likelihood
#logLik(garch_fit)
#logLik(garch_fit_cin)
# Akaike Information Criterion (AIC)
#AIC(garch_fit)
#AIC(garch_fit_cin)
# Bayesian Information Criterion (BIC)
#BIC(garch_fit)
#BIC(garch_fit_cin)# Verificar los métodos disponibles para el objeto garch_fit
methods(class = "uGARCHfit")## [1] coef confint convergence fitted
## [5] getspec gof halflife infocriteria
## [9] likelihood newsimpact nyblom persistence
## [13] pit plot quantile reduce
## [17] residuals show sigma signbias
## [21] ugarchboot ugarchdistribution ugarchforecast ugarchsim
## [25] uncmean uncvariance vcov
## see '?methods' for accessing help and source code# Obtener detalles completos del modelo GARCH para act_int_net
summary(garch_fit)## Length Class Mode
## 1 uGARCHfit S4# Obtener detalles completos del modelo GARCH para cin
summary(garch_fit_cin)## Length Class Mode
## 1 uGARCHfit S4info_garch_fit <- infocriteria(garch_fit)
print(info_garch_fit)##
## Akaike 7.361618
## Bayes 7.406861
## Shibata 7.361342
## Hannan-Quinn 7.379649info_garch_fit_cin <- infocriteria(garch_fit_cin)
print(info_garch_fit_cin)##
## Akaike 2.980786
## Bayes 3.026029
## Shibata 2.980510
## Hannan-Quinn 2.998817# Revisión de Cointegración
summary(vec) # Resumen de la prueba de cointegración##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 5.622967e-02 2.834612e-05
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.01 6.50 8.18 11.65
## r = 0 | 19.57 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l1 cin.l1
## act_int_net.l1 1.000000 1.00000
## cin.l1 5.526244 21.25998
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l1 cin.l1
## act_int_net.d 0.0006946586 7.931634e-04
## cin.d 0.0006741948 -7.984191e-05# Mostrar la estructura del modelo VAR
str(var)## List of 12
## $ deterministic: num [1:2, 1:4] 2.293 -0.515 -0.635 -0.528 -2.184 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "act_int_net" "cin"
## .. ..$ : chr [1:4] "constant" "sd1" "sd2" "sd3"
## $ A :List of 4
## ..$ A1: num [1:2, 1:2] 1.0684 -0.0126 0.8261 0.8075
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## ..$ A2: num [1:2, 1:2] -0.2811 0.0313 -2.7472 0.3314
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l2" "cin.l2"
## ..$ A3: num [1:2, 1:2] 0.1237 -0.00456 0.93287 0.0096
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l3" "cin.l3"
## ..$ A4: num [1:2, 1:2] 0.0897 -0.0134 0.9921 -0.1448
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l4" "cin.l4"
## $ p : int 4
## $ K : int 2
## $ y : Time-Series [1:342, 1:2] from 1996 to 2024: 57 57.9 58.5 57.8 58.6 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:2] "act_int_net" "cin"
## $ obs : int 338
## $ totobs : int 342
## $ call : language vec2var(z = vec, r = 1)
## $ vecm :Formal class 'ca.jo' [package "urca"] with 26 slots
## .. ..@ x : Time-Series [1:342, 1:2] from 1996 to 2024: 57 57.9 58.5 57.8 58.6 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. ..@ Z0 : num [1:338, 1:2] 0.816 -0.306 2.064 0.968 -0.656 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. ..@ Z1 : num [1:338, 1:10] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:10] "constant" "sd1" "sd2" "sd3" ...
## .. ..@ ZK : num [1:338, 1:2] 57.8 58.6 58.3 60.4 61.3 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ type : chr "trace statistic"
## .. ..@ model : chr "with linear trend"
## .. ..@ ecdet : chr "none"
## .. ..@ lag : int 4
## .. ..@ P : int 2
## .. ..@ season : num 4
## .. ..@ dumvar : NULL
## .. ..@ cval : num [1:2, 1:3] 6.5 15.66 8.18 17.95 11.65 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "r <= 1 |" "r = 0 |"
## .. .. .. ..$ : chr [1:3] "10pct" "5pct" "1pct"
## .. ..@ teststat : num [1:2] 0.00958 19.57046
## .. ..@ lambda : num [1:2] 5.62e-02 2.83e-05
## .. ..@ Vorg : num [1:2, 1:2] 0.00554 0.03064 0.00889 0.18894
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ V : num [1:2, 1:2] 1 5.53 1 21.26
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ W : num [1:2, 1:2] 6.95e-04 6.74e-04 7.93e-04 -7.98e-05
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ PI : num [1:2, 1:2] 0.001488 0.000594 0.020701 0.002028
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ DELTA : num [1:2, 1:2] 281.3 -28 -28 3.1
## .. ..@ GAMMA : num [1:2, 1:10] 1.339 -0.419 -0.63 -0.529 -2.182 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. .. .. ..$ : chr [1:10] "constant" "sd1" "sd2" "sd3" ...
## .. ..@ R0 : num [1:338, 1:2] -2.87 -2.67 1.86 -3.37 -4.35 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "R0.act_int_net.d" "R0.cin.d"
## .. ..@ RK : num [1:338, 1:2] -250 -280 -313 -335 -251 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "RK.act_int_net.l1" "RK.cin.l1"
## .. ..@ bp : logi NA
## .. ..@ spec : chr "transitory"
## .. ..@ call : language ca.jo(x = df_ts, type = "trace", ecdet = "none", K = 4, spec = "transitory", season = 4)
## .. ..@ test.name: chr "Johansen-Procedure"
## $ datamat : num [1:338, 1:14] 58.6 58.3 60.4 61.3 60.7 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:14] "act_int_net" "cin" "constant" "sd1" ...
## $ resid : num [1:338, 1:2] -2.75 -2.53 2.01 -3.21 -4.23 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:2] "resids of act_int_net" "resids of cin"
## $ r : int 1
## - attr(*, "class")= chr "vec2var"# Mostrar las matrices de coeficientes
coef_var <- coef(var)
print(coef_var)## NULL# Verificar la estructura del objeto
str(var)## List of 12
## $ deterministic: num [1:2, 1:4] 2.293 -0.515 -0.635 -0.528 -2.184 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "act_int_net" "cin"
## .. ..$ : chr [1:4] "constant" "sd1" "sd2" "sd3"
## $ A :List of 4
## ..$ A1: num [1:2, 1:2] 1.0684 -0.0126 0.8261 0.8075
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## ..$ A2: num [1:2, 1:2] -0.2811 0.0313 -2.7472 0.3314
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l2" "cin.l2"
## ..$ A3: num [1:2, 1:2] 0.1237 -0.00456 0.93287 0.0096
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l3" "cin.l3"
## ..$ A4: num [1:2, 1:2] 0.0897 -0.0134 0.9921 -0.1448
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. .. ..$ : chr [1:2] "act_int_net.l4" "cin.l4"
## $ p : int 4
## $ K : int 2
## $ y : Time-Series [1:342, 1:2] from 1996 to 2024: 57 57.9 58.5 57.8 58.6 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:2] "act_int_net" "cin"
## $ obs : int 338
## $ totobs : int 342
## $ call : language vec2var(z = vec, r = 1)
## $ vecm :Formal class 'ca.jo' [package "urca"] with 26 slots
## .. ..@ x : Time-Series [1:342, 1:2] from 1996 to 2024: 57 57.9 58.5 57.8 58.6 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "act_int_net" "cin"
## .. ..@ Z0 : num [1:338, 1:2] 0.816 -0.306 2.064 0.968 -0.656 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. ..@ Z1 : num [1:338, 1:10] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:10] "constant" "sd1" "sd2" "sd3" ...
## .. ..@ ZK : num [1:338, 1:2] 57.8 58.6 58.3 60.4 61.3 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ type : chr "trace statistic"
## .. ..@ model : chr "with linear trend"
## .. ..@ ecdet : chr "none"
## .. ..@ lag : int 4
## .. ..@ P : int 2
## .. ..@ season : num 4
## .. ..@ dumvar : NULL
## .. ..@ cval : num [1:2, 1:3] 6.5 15.66 8.18 17.95 11.65 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "r <= 1 |" "r = 0 |"
## .. .. .. ..$ : chr [1:3] "10pct" "5pct" "1pct"
## .. ..@ teststat : num [1:2] 0.00958 19.57046
## .. ..@ lambda : num [1:2] 5.62e-02 2.83e-05
## .. ..@ Vorg : num [1:2, 1:2] 0.00554 0.03064 0.00889 0.18894
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ V : num [1:2, 1:2] 1 5.53 1 21.26
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ W : num [1:2, 1:2] 6.95e-04 6.74e-04 7.93e-04 -7.98e-05
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ PI : num [1:2, 1:2] 0.001488 0.000594 0.020701 0.002028
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. .. .. ..$ : chr [1:2] "act_int_net.l1" "cin.l1"
## .. ..@ DELTA : num [1:2, 1:2] 281.3 -28 -28 3.1
## .. ..@ GAMMA : num [1:2, 1:10] 1.339 -0.419 -0.63 -0.529 -2.182 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "act_int_net.d" "cin.d"
## .. .. .. ..$ : chr [1:10] "constant" "sd1" "sd2" "sd3" ...
## .. ..@ R0 : num [1:338, 1:2] -2.87 -2.67 1.86 -3.37 -4.35 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "R0.act_int_net.d" "R0.cin.d"
## .. ..@ RK : num [1:338, 1:2] -250 -280 -313 -335 -251 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "RK.act_int_net.l1" "RK.cin.l1"
## .. ..@ bp : logi NA
## .. ..@ spec : chr "transitory"
## .. ..@ call : language ca.jo(x = df_ts, type = "trace", ecdet = "none", K = 4, spec = "transitory", season = 4)
## .. ..@ test.name: chr "Johansen-Procedure"
## $ datamat : num [1:338, 1:14] 58.6 58.3 60.4 61.3 60.7 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:14] "act_int_net" "cin" "constant" "sd1" ...
## $ resid : num [1:338, 1:2] -2.75 -2.53 2.01 -3.21 -4.23 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:2] "resids of act_int_net" "resids of cin"
## $ r : int 1
## - attr(*, "class")= chr "vec2var"# Acceder a las matrices de coeficientes si están disponibles
if (!is.null(var$A)) {
A1 <- var$A$A1
A2 <- var$A$A2
A3 <- var$A$A3
A4 <- var$A$A4
A5 <- var$A$A5
A6 <- var$A$A6
A7 <- var$A$A7
A8 <- var$A$A8
A9 <- var$A$A9
# Imprimir las matrices de coeficientes
print("Coeficientes A1:")
print(A1)
print("Coeficientes A2:")
print(A2)
print("Coeficientes A3:")
print(A3)
print("Coeficientes A4:")
print(A4)
print("Coeficientes A5:")
print(A5)
print("Coeficientes A6:")
print(A6)
print("Coeficientes A7:")
print(A7)
print("Coeficientes A8:")
print(A8)
print("Coeficientes A9:")
print(A9)
} else {
print("No se encontraron coeficientes en el objeto 'var'.")
}## [1] "Coeficientes A1:"
## act_int_net.l1 cin.l1
## act_int_net 1.06838034 0.8261167
## cin -0.01258539 0.8074789
## [1] "Coeficientes A2:"
## act_int_net.l2 cin.l2
## act_int_net -0.28112513 -2.7472436
## cin 0.03126265 0.3314435
## [1] "Coeficientes A3:"
## act_int_net.l3 cin.l3
## act_int_net 0.123704835 0.932865766
## cin -0.004561111 0.009600664
## [1] "Coeficientes A4:"
## act_int_net.l4 cin.l4
## act_int_net 0.08973462 0.9921000
## cin -0.01344195 -0.1447972
## [1] "Coeficientes A5:"
## NULL
## [1] "Coeficientes A6:"
## NULL
## [1] "Coeficientes A7:"
## NULL
## [1] "Coeficientes A8:"
## NULL
## [1] "Coeficientes A9:"
## NULL# Número de rezagos en el modelo VAR
lag_order <- 9
# Acceder a las matrices de coeficientes si están disponibles
if (!is.null(var$A)) {
# Recorremos los rezagos y calculamos los polos para cada uno
for (i in 1:lag_order) {
A <- var$A[[paste0("A", i)]]
if (!is.null(A)) {
polos <- eigen(A)$values
print(paste("Polos para el lag", i, ":"))
print(polos)
} else {
print(paste("No se encontró coeficiente para el rezago", i))
}
}
} else {
print("No se encontraron coeficientes en el objeto 'var'.")
}## [1] "Polos para el lag 1 :"
## [1] 1.0192954 0.8565639
## [1] "Polos para el lag 2 :"
## [1] 0.11417572 -0.06385739
## [1] "Polos para el lag 3 :"
## [1] 0.06665275+0.03162221i 0.06665275-0.03162221i
## [1] "Polos para el lag 4 :"
## [1] -0.047916083 -0.007146542
## [1] "No se encontró coeficiente para el rezago 5"
## [1] "No se encontró coeficiente para el rezago 6"
## [1] "No se encontró coeficiente para el rezago 7"
## [1] "No se encontró coeficiente para el rezago 8"
## [1] "No se encontró coeficiente para el rezago 9"# Número de rezagos en el modelo VAR
lag_order <- 4
# Acceder a las matrices de coeficientes si están disponibles
if (!is.null(var$A)) {
# Recorremos los rezagos y calculamos los polos para cada uno
for (i in 1:lag_order) {
A <- var$A[[paste0("A", i)]]
if (!is.null(A)) {
polos <- eigen(A)$values
print(paste("Polos para el lag", i, ":"))
print(polos)
} else {
print(paste("No se encontró coeficiente para el rezago", i))
}
}
} else {
print("No se encontraron coeficientes en el objeto 'var'.")
}## [1] "Polos para el lag 1 :"
## [1] 1.0192954 0.8565639
## [1] "Polos para el lag 2 :"
## [1] 0.11417572 -0.06385739
## [1] "Polos para el lag 3 :"
## [1] 0.06665275+0.03162221i 0.06665275-0.03162221i
## [1] "Polos para el lag 4 :"
## [1] -0.047916083 -0.007146542# Análisis de Residuos
# Residuos y modelos GARCH
residuos <- residuals(var)
garch_fit <- ugarchfit(spec = garch_spec, data = residuos[,1])
garch_fit_cin <- ugarchfit(spec = garch_spec, data = residuos[,2])
# Información de GARCH
info_garch_fit <- infocriteria(garch_fit)
info_garch_fit_cin <- infocriteria(garch_fit_cin)
# Funciones de Impulso-Respuesta
ir <- vars::irf(var, n.ahead = 20, impulse = "act_int_net", response = "cin")
plot(ir)
ir1 <- vars::irf(var, n.ahead = 20, impulse = "cin", response = "act_int_net")
plot(ir1)
# Decomposición de la Varianza
fevd.R <- vars::fevd(var, n.ahead = 9)
plot(fevd.R)
# Predicciones
predictions <- predict(var, n.ahead = 9, ci = 0.95)
plot(predictions)
# Revisión de residuos ajustados y pruebas unitarias
rt <- df_ts[,1] - 3.9619 * df_ts[,2]
adf.test(rt)##
## Augmented Dickey-Fuller Test
##
## data: rt
## Dickey-Fuller = -1.9946, Lag order = 6, p-value = 0.579
## alternative hypothesis: stationarypp.test(rt)##
## Phillips-Perron Unit Root Test
##
## data: rt
## Dickey-Fuller Z(alpha) = -10.943, Truncation lag parameter = 5, p-value
## = 0.4973
## alternative hypothesis: stationarysummary(ur.df(rt))##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression none
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -95.105 -4.201 1.218 7.794 186.048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## z.lag.1 0.002422 0.003113 0.778 0.437
## z.diff.lag -0.022306 0.055622 -0.401 0.689
##
## Residual standard error: 23.87 on 338 degrees of freedom
## Multiple R-squared: 0.002126, Adjusted R-squared: -0.003779
## F-statistic: 0.36 on 2 and 338 DF, p-value: 0.6979
##
##
## Value of test-statistic is: 0.7779
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau1 -2.58 -1.95 -1.62#############################333
# Diferenciar la serie
rt_diff <- diff(rt)
# Augmented Dickey-Fuller Test
adf_test_diff <- adf.test(rt_diff)## Warning in adf.test(rt_diff): p-value smaller than printed p-valueprint("Augmented Dickey-Fuller Test para la serie diferenciada:")## [1] "Augmented Dickey-Fuller Test para la serie diferenciada:"print(adf_test_diff)##
## Augmented Dickey-Fuller Test
##
## data: rt_diff
## Dickey-Fuller = -7.3747, Lag order = 6, p-value = 0.01
## alternative hypothesis: stationary# Phillips-Perron Unit Root Test
pp_test_diff <- pp.test(rt_diff)## Warning in pp.test(rt_diff): p-value smaller than printed p-valueprint("Phillips-Perron Unit Root Test para la serie diferenciada:")## [1] "Phillips-Perron Unit Root Test para la serie diferenciada:"print(pp_test_diff)##
## Phillips-Perron Unit Root Test
##
## data: rt_diff
## Dickey-Fuller Z(alpha) = -330.1, Truncation lag parameter = 5, p-value
## = 0.01
## alternative hypothesis: stationary# Summary of ur.df
ur_df_diff <- ur.df(rt_diff, type = "none", lags = 1)
print("Resumen de la prueba ur.df para la serie diferenciada:")## [1] "Resumen de la prueba ur.df para la serie diferenciada:"print(summary(ur_df_diff))##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression none
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -93.273 -3.879 1.288 8.615 188.322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## z.lag.1 -1.03875 0.07934 -13.092 <2e-16 ***
## z.diff.lag 0.01957 0.05557 0.352 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.92 on 337 degrees of freedom
## Multiple R-squared: 0.4995, Adjusted R-squared: 0.4966
## F-statistic: 168.2 on 2 and 337 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -13.0919
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau1 -2.58 -1.95 -1.62# Cargar librerías necesarias
# Supongamos que tu modelo VAR ya está ajustado y almacenado en 'var'
# Ajustar el modelo VECM con 4 rezagos
vecm_model <- ca.jo(df_ts, type = "trace", ecdet = "none", K = 4)
# Resumen del modelo VECM
summary(vecm_model)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 6.809512e-02 2.776331e-05
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.01 6.50 8.18 11.65
## r = 0 | 23.85 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.000000 1.00000
## cin.l4 6.096282 21.51085
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d 0.0006789021 7.75137e-04
## cin.d 0.0008458887 -7.79357e-05# Pruebas de Cointegración
# Prueba de Johansen para la cointegración
johansen_test <- ca.jo(df_ts, type = "trace", K = 4, ecdet = "none")
summary(johansen_test)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 6.809512e-02 2.776331e-05
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.01 6.50 8.18 11.65
## r = 0 | 23.85 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.000000 1.00000
## cin.l4 6.096282 21.51085
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d 0.0006789021 7.75137e-04
## cin.d 0.0008458887 -7.79357e-05#Hasta aqui vamos bien con la relacion de largo plazo, 4 rezagos fueron optimos# Ajustar el modelo VAR al sistema de vectores cointegrados
var_model <- vec2var(vecm_model, r = 1) # Ajusta el modelo VAR con el número de relaciones de cointegración (r)
# Obtener residuos del modelo VAR
residuos_vecm <- residuals(var_model)
# Ver los primeros residuos para confirmación
head(residuos_vecm)## resids of act_int_net resids of cin
## [1,] -1.6378506 0.23653596
## [2,] -2.9925344 0.32580492
## [3,] -0.4972903 0.07160549
## [4,] -1.4368936 0.13850397
## [5,] -3.1027195 0.32112942
## [6,] -0.4499783 0.09804613# Especificar el modelo GARCH (ejemplo: GARCH(1,1))
garch_spec <- ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),
mean.model = list(armaOrder = c(0, 0)),
distribution.model = "std")
# Ajustar el modelo GARCH a los residuos
garch_fit <- ugarchfit(spec = garch_spec, data = residuos_vecm[, 1])
garch_fit_cin <- ugarchfit(spec = garch_spec, data = residuos_vecm[, 2])
# Información del ajuste GARCH
info_garch_fit <- infocriteria(garch_fit)
info_garch_fit_cin <- infocriteria(garch_fit_cin)
# Imprimir la información
print(info_garch_fit)##
## Akaike 7.271013
## Bayes 7.327567
## Shibata 7.270584
## Hannan-Quinn 7.293552print(info_garch_fit_cin)##
## Akaike 2.947892
## Bayes 3.004446
## Shibata 2.947463
## Hannan-Quinn 2.970432# Revisar los residuos estandarizados del GARCH
plot(garch_fit, which = "all")##
## please wait...calculating quantiles...
plot(garch_fit_cin, which = "all")##
## please wait...calculating quantiles...
# Predicciones de volatilidad
forecast_garch <- ugarchforecast(garch_fit, n.ahead = 10)
forecast_garch_cin <- ugarchforecast(garch_fit_cin, n.ahead = 10)
# Mostrar las predicciones
plot(forecast_garch, which = 1)
plot(forecast_garch_cin, which = 1)
# Predicciones de volatilidad
forecast_garch <- ugarchforecast(garch_fit, n.ahead = 10)
forecast_garch_cin <- ugarchforecast(garch_fit_cin, n.ahead = 10)
# Mostrar las predicciones
plot(forecast_garch, which = 1)
plot(forecast_garch_cin, which = 1)
# Funciones de Impulso-Respuesta
ir <- vars::irf(var_model, n.ahead = 20, impulse = "act_int_net", response = "cin")
plot(ir)
ir1 <- vars::irf(var_model, n.ahead = 20, impulse = "cin", response = "act_int_net")
plot(ir1)
# Decomposición de la Varianza
fevd.R <- vars::fevd(var_model, n.ahead = 9)
plot(fevd.R)
# Revisar la información de los residuos
#residuos_vecm <- residuals(vecm_model)
# Modelos GARCH para los residuos del modelo VECM
#library(rugarch)
#garch_spec <- ugarchspec()
#garch_fit_act_int_net <- ugarchfit(spec = garch_spec, data = residuos_vecm[,1])
#garch_fit_cin <- ugarchfit(spec = garch_spec, data = residuos_vecm[,2])
# Información de los modelos GARCH
#info_garch_fit_act_int_net <- infocriteria(garch_fit_act_int_net)
#info_garch_fit_cin <- infocriteria(garch_fit_cin)
# Funciones de Impulso-Respuesta
#ir_vecm <- vars::irf(vecm_model, n.ahead = 20, impulse = "act_int_net", response = "cin")
#plot(ir_vecm)
#ir_vecm1 <- vars::irf(vecm_model, n.ahead = 20, impulse = "cin", response = "act_int_net")
#plot(ir_vecm1)
# Decomposición de la Varianza
#fevd_vecm <- vars::fevd(vecm_model, n.ahead = 9)
#plot(fevd_vecm)
# Predicciones del modelo VECM
#predictions_vecm <- predict(vecm_model, n.ahead = 9, ci = 0.95)
#plot(predictions_vecm)
# Revisión de residuos ajustados y pruebas unitarias
# Crear serie diferenciada
#rt_diff <- diff(rt)
# Pruebas unitarias para la serie diferenciada
#adf_test_diff <- adf.test(rt_diff)
#pp_test_diff <- pp.test(rt_diff)
#ur_df_diff <- ur.df(rt_diff, type = "none", lags = 1)
# Imprimir resultados de pruebas unitarias
#print("Augmented Dickey-Fuller Test para la serie diferenciada:")
#print(adf_test_diff)
#print("Phillips-Perron Unit Root Test para la serie diferenciada:")
#print(pp_test_diff)
#print("Resumen de la prueba ur.df para la serie diferenciada:")
#print(summary(ur_df_diff))# Ver los periodos de tiempo
time_periods <- time(df_ts)
print(time_periods)## Jan Feb Mar Apr May Jun Jul Aug
## 1996 1996.000 1996.083 1996.167 1996.250 1996.333 1996.417 1996.500 1996.583
## 1997 1997.000 1997.083 1997.167 1997.250 1997.333 1997.417 1997.500 1997.583
## 1998 1998.000 1998.083 1998.167 1998.250 1998.333 1998.417 1998.500 1998.583
## 1999 1999.000 1999.083 1999.167 1999.250 1999.333 1999.417 1999.500 1999.583
## 2000 2000.000 2000.083 2000.167 2000.250 2000.333 2000.417 2000.500 2000.583
## 2001 2001.000 2001.083 2001.167 2001.250 2001.333 2001.417 2001.500 2001.583
## 2002 2002.000 2002.083 2002.167 2002.250 2002.333 2002.417 2002.500 2002.583
## 2003 2003.000 2003.083 2003.167 2003.250 2003.333 2003.417 2003.500 2003.583
## 2004 2004.000 2004.083 2004.167 2004.250 2004.333 2004.417 2004.500 2004.583
## 2005 2005.000 2005.083 2005.167 2005.250 2005.333 2005.417 2005.500 2005.583
## 2006 2006.000 2006.083 2006.167 2006.250 2006.333 2006.417 2006.500 2006.583
## 2007 2007.000 2007.083 2007.167 2007.250 2007.333 2007.417 2007.500 2007.583
## 2008 2008.000 2008.083 2008.167 2008.250 2008.333 2008.417 2008.500 2008.583
## 2009 2009.000 2009.083 2009.167 2009.250 2009.333 2009.417 2009.500 2009.583
## 2010 2010.000 2010.083 2010.167 2010.250 2010.333 2010.417 2010.500 2010.583
## 2011 2011.000 2011.083 2011.167 2011.250 2011.333 2011.417 2011.500 2011.583
## 2012 2012.000 2012.083 2012.167 2012.250 2012.333 2012.417 2012.500 2012.583
## 2013 2013.000 2013.083 2013.167 2013.250 2013.333 2013.417 2013.500 2013.583
## 2014 2014.000 2014.083 2014.167 2014.250 2014.333 2014.417 2014.500 2014.583
## 2015 2015.000 2015.083 2015.167 2015.250 2015.333 2015.417 2015.500 2015.583
## 2016 2016.000 2016.083 2016.167 2016.250 2016.333 2016.417 2016.500 2016.583
## 2017 2017.000 2017.083 2017.167 2017.250 2017.333 2017.417 2017.500 2017.583
## 2018 2018.000 2018.083 2018.167 2018.250 2018.333 2018.417 2018.500 2018.583
## 2019 2019.000 2019.083 2019.167 2019.250 2019.333 2019.417 2019.500 2019.583
## 2020 2020.000 2020.083 2020.167 2020.250 2020.333 2020.417 2020.500 2020.583
## 2021 2021.000 2021.083 2021.167 2021.250 2021.333 2021.417 2021.500 2021.583
## 2022 2022.000 2022.083 2022.167 2022.250 2022.333 2022.417 2022.500 2022.583
## 2023 2023.000 2023.083 2023.167 2023.250 2023.333 2023.417 2023.500 2023.583
## 2024 2024.000 2024.083 2024.167 2024.250 2024.333 2024.417
## Sep Oct Nov Dec
## 1996 1996.667 1996.750 1996.833 1996.917
## 1997 1997.667 1997.750 1997.833 1997.917
## 1998 1998.667 1998.750 1998.833 1998.917
## 1999 1999.667 1999.750 1999.833 1999.917
## 2000 2000.667 2000.750 2000.833 2000.917
## 2001 2001.667 2001.750 2001.833 2001.917
## 2002 2002.667 2002.750 2002.833 2002.917
## 2003 2003.667 2003.750 2003.833 2003.917
## 2004 2004.667 2004.750 2004.833 2004.917
## 2005 2005.667 2005.750 2005.833 2005.917
## 2006 2006.667 2006.750 2006.833 2006.917
## 2007 2007.667 2007.750 2007.833 2007.917
## 2008 2008.667 2008.750 2008.833 2008.917
## 2009 2009.667 2009.750 2009.833 2009.917
## 2010 2010.667 2010.750 2010.833 2010.917
## 2011 2011.667 2011.750 2011.833 2011.917
## 2012 2012.667 2012.750 2012.833 2012.917
## 2013 2013.667 2013.750 2013.833 2013.917
## 2014 2014.667 2014.750 2014.833 2014.917
## 2015 2015.667 2015.750 2015.833 2015.917
## 2016 2016.667 2016.750 2016.833 2016.917
## 2017 2017.667 2017.750 2017.833 2017.917
## 2018 2018.667 2018.750 2018.833 2018.917
## 2019 2019.667 2019.750 2019.833 2019.917
## 2020 2020.667 2020.750 2020.833 2020.917
## 2021 2021.667 2021.750 2021.833 2021.917
## 2022 2022.667 2022.750 2022.833 2022.917
## 2023 2023.667 2023.750 2023.833 2023.917
## 2024# Definir los periodos específicos (ejemplo: del 2000 al 2010)
periodo1 <- window(df_ts, start = c(1996, 1), end = c(2009, 12))
periodo2 <- window(df_ts, start = c(2010, 1), end = c(2015, 12))
periodo3 <- window(df_ts, start = c(2016, 1), end = c(2024, 6))
# Verificar que los datos hayan sido filtrados correctamente
print(head(periodo1))## act_int_net cin
## Jan 1996 57.00000 57.00000
## Feb 1996 57.87973 56.91020
## Mar 1996 58.50433 56.87984
## Apr 1996 57.80657 56.90514
## May 1996 58.62242 56.88744
## Jun 1996 58.31641 56.93827print(head(periodo2))## act_int_net cin
## Jan 2010 300.7956 43.02449
## Feb 2010 299.5884 42.83627
## Mar 2010 294.9121 43.54895
## Apr 2010 298.7340 42.73519
## May 2010 311.7850 41.59269
## Jun 2010 314.5462 41.22656print(head(periodo3))## act_int_net cin
## Jan 2016 667.6302 17.95468
## Feb 2016 671.7605 17.29536
## Mar 2016 646.6003 20.24937
## Apr 2016 652.4276 19.41229
## May 2016 685.6036 16.53627
## Jun 2016 685.5759 16.80555# Ajustar VECM para el primer periodo
vecm_model1 <- ca.jo(periodo1, type = "trace", ecdet = "none", K = 4)
summary(vecm_model1)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 0.09705942 0.01131053
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 1.87 6.50 8.18 11.65
## r = 0 | 18.61 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 17.64977 12.68518
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d 0.1618759 0.0208101216
## cin.d -0.0209266 -0.0003853582# Ajustar VECM para el segundo periodo
vecm_model2 <- ca.jo(periodo2, type = "trace", ecdet = "none", K = 4)
summary(vecm_model2)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 0.1089061021 0.0009570726
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.07 6.50 8.18 11.65
## r = 0 | 7.91 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 15.28509 12.90504
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d 0.1420760 0.031080220
## cin.d -0.0255352 -0.001764339# Ajustar VECM para el tercer periodo
vecm_model3 <- ca.jo(periodo3, type = "trace", ecdet = "none", K = 4)
summary(vecm_model3)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 0.06884031 0.00451015
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.44 6.50 8.18 11.65
## r = 0 | 7.43 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.000000 1.00000
## cin.l4 -1.896856 18.96352
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d -0.08444266 0.0067782223
## cin.d 0.01044712 -0.0003974512# Supongamos que tienes tu modelo VECM ya ajustado y almacenado en un objeto llamado `vecm_model`.
# Extraer las matrices alpha y beta
alpha_matrix <- vecm_model@V
beta_matrix <- vecm_model@W
# Mostrar la matriz beta (relaciones de cointegración)
cat("Matriz Beta:\n")## Matriz Beta:print(beta_matrix)## act_int_net.l4 cin.l4
## act_int_net.d 0.0006789021 7.75137e-04
## cin.d 0.0008458887 -7.79357e-05# Mostrar la matriz alpha (velocidades de ajuste)
cat("Matriz Alpha:\n")## Matriz Alpha:print(alpha_matrix)## act_int_net.l4 cin.l4
## act_int_net.l4 1.000000 1.00000
## cin.l4 6.096282 21.51085######Matrices por periodo
#################Ajustar VECM para el primer periodo
vecm_model1 <- ca.jo(periodo1, type = "trace", ecdet = "none", K = 4)
summary(vecm_model1)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 0.09705942 0.01131053
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 1.87 6.50 8.18 11.65
## r = 0 | 18.61 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 17.64977 12.68518
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d 0.1618759 0.0208101216
## cin.d -0.0209266 -0.0003853582alpha1 <- vecm_model1@V
beta1 <- vecm_model1@W
# Ajustar VECM para el segundo periodo
vecm_model2 <- ca.jo(periodo2, type = "trace", ecdet = "none", K = 4)
summary(vecm_model2)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 0.1089061021 0.0009570726
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.07 6.50 8.18 11.65
## r = 0 | 7.91 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 15.28509 12.90504
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d 0.1420760 0.031080220
## cin.d -0.0255352 -0.001764339alpha2 <- vecm_model2@V
beta2 <- vecm_model2@W
# Ajustar VECM para el tercer periodo
vecm_model3 <- ca.jo(periodo3, type = "trace", ecdet = "none", K = 4)
summary(vecm_model3)##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , with linear trend
##
## Eigenvalues (lambda):
## [1] 0.06884031 0.00451015
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 0.44 6.50 8.18 11.65
## r = 0 | 7.43 15.66 17.95 23.52
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## act_int_net.l4 cin.l4
## act_int_net.l4 1.000000 1.00000
## cin.l4 -1.896856 18.96352
##
## Weights W:
## (This is the loading matrix)
##
## act_int_net.l4 cin.l4
## act_int_net.d -0.08444266 0.0067782223
## cin.d 0.01044712 -0.0003974512alpha3 <- vecm_model3@V
beta3 <- vecm_model3@W
# Mostrar las matrices alfa y beta para cada periodo
# Periodo 1
cat("Periodo 1 (2000-2010)\n")## Periodo 1 (2000-2010)cat("Matriz Beta:\n")## Matriz Beta:print(beta1)## act_int_net.l4 cin.l4
## act_int_net.d 0.1618759 0.0208101216
## cin.d -0.0209266 -0.0003853582cat("Matriz Alpha:\n")## Matriz Alpha:print(alpha1)## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 17.64977 12.68518# Periodo 2
cat("Periodo 2 (2011-2020)\n")## Periodo 2 (2011-2020)cat("Matriz Beta:\n")## Matriz Beta:print(beta2)## act_int_net.l4 cin.l4
## act_int_net.d 0.1420760 0.031080220
## cin.d -0.0255352 -0.001764339cat("Matriz Alpha:\n")## Matriz Alpha:print(alpha2)## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 15.28509 12.90504# Periodo 3
cat("Periodo 3 (2021-2023)\n")## Periodo 3 (2021-2023)cat("Matriz Beta:\n")## Matriz Beta:print(beta3)## act_int_net.l4 cin.l4
## act_int_net.d -0.08444266 0.0067782223
## cin.d 0.01044712 -0.0003974512cat("Matriz Alpha:\n")## Matriz Alpha:print(alpha3)## act_int_net.l4 cin.l4
## act_int_net.l4 1.000000 1.00000
## cin.l4 -1.896856 18.96352# Periodo 1
cat("Periodo 1 (2000-2010)\n")## Periodo 1 (2000-2010)cat("Matriz Beta (Cointegration vectors):\n")## Matriz Beta (Cointegration vectors):print(vecm_model1@V) # Cointegration vectors## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 17.64977 12.68518cat("Matriz Alpha (Loading vectors):\n")## Matriz Alpha (Loading vectors):print(vecm_model1@W) # Loading vectors## act_int_net.l4 cin.l4
## act_int_net.d 0.1618759 0.0208101216
## cin.d -0.0209266 -0.0003853582# Periodo 2
cat("Periodo 2 (2011-2020)\n")## Periodo 2 (2011-2020)cat("Matriz Beta (Cointegration vectors):\n")## Matriz Beta (Cointegration vectors):print(vecm_model2@V) ## act_int_net.l4 cin.l4
## act_int_net.l4 1.00000 1.00000
## cin.l4 15.28509 12.90504cat("Matriz Alpha (Loading vectors):\n")## Matriz Alpha (Loading vectors):print(vecm_model2@W) ## act_int_net.l4 cin.l4
## act_int_net.d 0.1420760 0.031080220
## cin.d -0.0255352 -0.001764339# Periodo 3
cat("Periodo 3 (2021-2023)\n")## Periodo 3 (2021-2023)cat("Matriz Beta (Cointegration vectors):\n")## Matriz Beta (Cointegration vectors):print(vecm_model3@V)## act_int_net.l4 cin.l4
## act_int_net.l4 1.000000 1.00000
## cin.l4 -1.896856 18.96352cat("Matriz Alpha (Loading vectors):\n")## Matriz Alpha (Loading vectors):print(vecm_model3@W)## act_int_net.l4 cin.l4
## act_int_net.d -0.08444266 0.0067782223
## cin.d 0.01044712 -0.0003974512