DATA A USAR
DATA_CA_y_PAN <- read_excel("C:/Users/8abla/Documents/MAE118/Tarea/IVAE CA y PAN.xlsx",
col_types = c("skip", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric"), skip = 4)
colnames(DATA_CA_y_PAN)<-c("Costa Rica", "El Salvador", "Guatemala", "Honduras", "Nicaragua", "Panamá")
COSTA RICA
Datos
DATA_CA_y_PAN %>% select("Costa Rica") %>% as.data.frame() %>% na.omit()->IVAE_CRC
IVAE_CRC<- ts(data = IVAE_CRC, start = c(2009,1), frequency = 12)
print(IVAE_CRC)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2009 73.05 70.50 75.68 70.13 72.30 73.43 72.93 72.00 73.64 76.77
## 2010 75.10 73.53 79.92 73.27 75.74 76.43 76.13 75.58 77.14 79.74
## 2011 78.27 76.77 82.00 76.03 79.23 79.63 77.99 77.89 80.05 83.57
## 2012 82.37 82.95 86.03 78.55 82.23 81.83 80.60 81.77 82.75 85.69
## 2013 83.10 82.79 85.62 81.13 84.12 83.77 83.88 83.97 86.04 88.53
## 2014 86.41 87.04 89.12 83.12 86.04 85.36 86.63 86.17 88.14 92.55
## 2015 88.30 90.04 92.86 88.50 92.09 92.53 93.84 92.75 93.78 96.67
## 2016 94.53 95.60 96.36 93.13 95.39 95.66 94.94 94.84 98.12 101.26
## 2017 96.71 96.96 100.85 94.84 99.06 99.90 96.26 96.64 98.99 103.96
## 2018 99.21 99.00 103.55 99.62 104.59 103.43 101.46 101.10 101.62 106.09
## 2019 101.48 101.93 105.94 99.98 103.78 103.63 102.45 101.43 103.57 109.05
## 2020 102.20 104.23 102.60 89.65 91.81 95.78 91.86 92.51 97.39 101.72
## 2021 96.63 100.29 108.09 101.66 104.50 104.73 107.77 105.71 108.62 111.23
## 2022 106.31 108.14 117.49 105.61 108.90 109.10 110.06 110.34
## Nov Dec
## 2009 78.18 78.35
## 2010 82.16 81.06
## 2011 85.93 84.67
## 2012 89.26 88.63
## 2013 90.77 90.80
## 2014 94.00 95.23
## 2015 98.43 97.87
## 2016 103.90 103.79
## 2017 107.71 108.11
## 2018 108.90 108.01
## 2019 111.47 111.09
## 2020 105.12 110.61
## 2021 116.91 119.84
## 2022
autoplot(IVAE_CRC,xlab = "años",ylab = "Indice",main = "IVAE-Costa Rica total, periodo 2009-2022 (Agosto)")+theme_classic()
Proyección a Seis meses
modelo_CRC<-auto.arima(y = IVAE_CRC)
summary(modelo_CRC)
## Series: IVAE_CRC
## ARIMA(1,0,0)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 sma1 drift
## 0.8563 -0.5052 0.2377
## s.e. 0.0419 0.0810 0.0442
##
## sigma^2 = 3.366: log likelihood = -308.74
## AIC=625.48 AICc=625.76 BIC=637.58
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.01259946 1.748727 1.157394 -0.00975326 1.211633 0.2864043
## ACF1
## Training set -0.0658692
pronosticos_CRC<-forecast(modelo_CRC,h = 6)
autoplot(pronosticos_CRC)+xlab("Años")+ylab("indice")+theme_bw()
autoplot(pronosticos_CRC$x,series = "IVAE")+autolayer(pronosticos_CRC$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
Serie ampliada
IVAE_CRC_Amp<-ts(as.numeric(rbind(as.matrix(pronosticos_CRC$x),as.matrix(pronosticos_CRC$mean))),start = c(2009,1),frequency = 12)
print(IVAE_CRC_Amp)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 73.0500 70.5000 75.6800 70.1300 72.3000 73.4300 72.9300 72.0000
## 2010 75.1000 73.5300 79.9200 73.2700 75.7400 76.4300 76.1300 75.5800
## 2011 78.2700 76.7700 82.0000 76.0300 79.2300 79.6300 77.9900 77.8900
## 2012 82.3700 82.9500 86.0300 78.5500 82.2300 81.8300 80.6000 81.7700
## 2013 83.1000 82.7900 85.6200 81.1300 84.1200 83.7700 83.8800 83.9700
## 2014 86.4100 87.0400 89.1200 83.1200 86.0400 85.3600 86.6300 86.1700
## 2015 88.3000 90.0400 92.8600 88.5000 92.0900 92.5300 93.8400 92.7500
## 2016 94.5300 95.6000 96.3600 93.1300 95.3900 95.6600 94.9400 94.8400
## 2017 96.7100 96.9600 100.8500 94.8400 99.0600 99.9000 96.2600 96.6400
## 2018 99.2100 99.0000 103.5500 99.6200 104.5900 103.4300 101.4600 101.1000
## 2019 101.4800 101.9300 105.9400 99.9800 103.7800 103.6300 102.4500 101.4300
## 2020 102.2000 104.2300 102.6000 89.6500 91.8100 95.7800 91.8600 92.5100
## 2021 96.6300 100.2900 108.0900 101.6600 104.5000 104.7300 107.7700 105.7100
## 2022 106.3100 108.1400 117.4900 105.6100 108.9000 109.1000 110.0600 110.3400
## 2023 109.9052 111.7756
## Sep Oct Nov Dec
## 2009 73.6400 76.7700 78.1800 78.3500
## 2010 77.1400 79.7400 82.1600 81.0600
## 2011 80.0500 83.5700 85.9300 84.6700
## 2012 82.7500 85.6900 89.2600 88.6300
## 2013 86.0400 88.5300 90.7700 90.8000
## 2014 88.1400 92.5500 94.0000 95.2300
## 2015 93.7800 96.6700 98.4300 97.8700
## 2016 98.1200 101.2600 103.9000 103.7900
## 2017 98.9900 103.9600 107.7100 108.1100
## 2018 101.6200 106.0900 108.9000 108.0100
## 2019 103.5700 109.0500 111.4700 111.0900
## 2020 97.3900 101.7200 105.1200 110.6100
## 2021 108.6200 111.2300 116.9100 119.8400
## 2022 112.9157 116.0689 120.0131 122.4011
## 2023
Descomposición de la Serie Temporal
FIT_CRC<-stl(IVAE_CRC_Amp,"periodic")
autoplot(FIT_CRC)+theme_classic()
TC_CRC<-FIT_CRC$time.series[,2]
print(TC_CRC)
## Jan Feb Mar Apr May Jun Jul
## 2009 73.55845 73.66361 73.76877 73.87732 73.98587 74.10813 74.23039
## 2010 75.56108 75.90012 76.23916 76.55988 76.88060 77.12051 77.36042
## 2011 78.62940 78.90955 79.18971 79.49489 79.80008 80.10809 80.41611
## 2012 82.17139 82.44383 82.71628 82.95071 83.18514 83.33500 83.48487
## 2013 84.20715 84.44451 84.68187 84.91242 85.14298 85.37199 85.60099
## 2014 86.87594 87.08498 87.29402 87.55247 87.81092 88.07629 88.34166
## 2015 90.79583 91.34470 91.89358 92.35602 92.81847 93.20111 93.58375
## 2016 95.28315 95.56729 95.85142 96.23567 96.61993 96.97517 97.33041
## 2017 98.74523 98.91736 99.08948 99.29462 99.49976 99.77008 100.04040
## 2018 101.95512 102.30107 102.64701 102.85097 103.05493 103.18860 103.32226
## 2019 103.65794 103.78257 103.90719 104.10184 104.29649 104.48535 104.67421
## 2020 101.93393 101.06685 100.19977 99.50832 98.81686 98.45479 98.09272
## 2021 102.02633 103.05512 104.08391 105.01445 105.94498 106.77451 107.60404
## 2022 110.65562 110.93377 111.21191 111.51284 111.81378 112.11677 112.41977
## 2023 114.05214 114.33706
## Aug Sep Oct Nov Dec
## 2009 74.38098 74.53157 74.75088 74.97019 75.26563
## 2010 77.53958 77.71875 77.92781 78.13686 78.38313
## 2011 80.72591 81.03570 81.33322 81.63075 81.90107
## 2012 83.54768 83.61050 83.72350 83.83651 84.02183
## 2013 85.84741 86.09383 86.31105 86.52827 86.70210
## 2014 88.62847 88.91528 89.32809 89.74090 90.26837
## 2015 93.92133 94.25891 94.54322 94.82753 95.05534
## 2016 97.57679 97.82317 98.06297 98.30277 98.52400
## 2017 100.29498 100.54955 100.86216 101.17477 101.56495
## 2018 103.42024 103.51822 103.54701 103.57580 103.61687
## 2019 104.64153 104.60884 104.10143 103.59401 102.76397
## 2020 98.25937 98.42602 99.18059 99.93516 100.98075
## 2021 108.31104 109.01804 109.49925 109.98047 110.31805
## 2022 112.67839 112.93700 113.20927 113.48153 113.76683
## 2023
Calculo de las Tasa
TC_CRC %>% as.numeric() %>% as.data.frame()->TC_CRC_df
names(TC_CRC_df)<-c("TC_CRC")
TC_CRC_df %>% mutate(T_1_1=(TC_CRC/dplyr::lag(TC_CRC,n=1)-1)*100,
T_1_12=(TC_CRC/dplyr::lag(TC_CRC, n=12)-1)*100,
T_12_12=(rollapply(TC_CRC,12,mean,align="right",
fill=NA)/rollapply(dplyr::lag(TC_CRC, n=12), 12,mean,align="right", fill=NA)-1)*100) %>%
mutate(T_1_12C=dplyr::lead(T_1_12, n=6),
T_12_12C=dplyr::lead(T_12_12, n=12)) %>%
ts(start = c(2009,1), frequency = 12)->Coyun_CRC
print(tail(Coyun_CRC, n=12))
## TC_CRC T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## Mar 2022 111.2119 0.2507289 6.848319 8.697905 3.594789 NA
## Apr 2022 111.5128 0.2705948 6.188098 8.740460 3.388166 NA
## May 2022 111.8138 0.2698646 5.539474 8.585336 3.183350 NA
## Jun 2022 112.1168 0.2709813 5.003311 8.283174 3.126223 NA
## Jul 2022 112.4198 0.2702490 4.475416 7.837832 3.069444 NA
## Aug 2022 112.6784 0.2300474 4.032229 7.316447 3.067861 NA
## Sep 2022 112.9370 0.2295194 3.594789 6.721524 NA NA
## Oct 2022 113.2093 0.2410743 3.388166 6.143015 NA NA
## Nov 2022 113.4815 0.2404945 3.183350 5.580147 NA NA
## Dec 2022 113.7668 0.2514104 3.126223 5.080038 NA NA
## Jan 2023 114.0521 0.2507799 3.069444 4.640502 NA NA
## Feb 2023 114.3371 0.2498195 3.067861 4.267587 NA NA
Gráfico de las Tasas (Centradas)
Coyun_CRC %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->Graficos_CRC
autoplot(Graficos_CRC)+theme_bw()
Coyun_CRC %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()
EL SALVADOR
Datos
DATA_CA_y_PAN %>% select("El Salvador") %>% as.data.frame() %>% na.omit()->IVAE_CRC
IVAE_ESA<- ts(data = IVAE_CRC, start = c(2009,1), frequency = 12)
print(IVAE_ESA)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2009 86.73 80.85 87.19 83.92 91.42 93.46 86.39 86.72 87.57 85.27
## 2010 85.56 84.69 90.90 85.94 94.33 92.23 87.18 90.25 89.00 88.74
## 2011 90.27 86.73 94.32 90.79 98.50 97.59 92.16 94.22 92.33 89.06
## 2012 92.65 91.20 98.46 91.23 102.83 102.84 93.61 98.21 93.94 93.49
## 2013 95.67 90.77 96.12 96.34 103.08 101.58 96.42 98.96 97.74 96.22
## 2014 98.70 94.70 101.30 97.12 103.86 104.73 98.48 98.60 98.25 96.43
## 2015 98.87 94.82 103.15 98.75 105.65 105.45 101.67 101.06 100.64 100.44
## 2016 99.25 97.76 102.58 103.43 107.76 110.71 104.01 106.24 104.83 102.04
## 2017 101.41 98.97 108.44 101.40 110.85 113.63 105.51 107.88 106.21 103.28
## 2018 105.17 102.53 108.39 107.93 112.46 113.55 108.80 111.94 107.54 105.81
## 2019 108.10 106.41 113.02 109.95 114.95 114.86 111.24 113.28 111.66 108.32
## 2020 109.49 109.27 104.04 87.36 89.33 96.05 96.95 103.34 106.72 106.12
## 2021 106.84 107.04 114.51 109.72 115.43 115.19 112.16 114.23 113.82 109.73
## 2022 109.25 110.28 118.85 111.15 120.33 118.27 113.36 116.30
## Nov Dec
## 2009 91.86 99.64
## 2010 93.13 100.74
## 2011 96.86 103.91
## 2012 99.61 105.05
## 2013 101.24 108.37
## 2014 100.64 107.19
## 2015 104.90 109.86
## 2016 106.50 114.98
## 2017 110.39 117.56
## 2018 112.16 120.03
## 2019 116.10 122.08
## 2020 110.70 119.86
## 2021 116.70 123.69
## 2022
autoplot(IVAE_ESA,xlab = "años",ylab = "Indice",main = "IVAE-El Salvador total, periodo 2009-2022 (Agosto)")+theme_classic()
Proyección a Seis meses
modelo_ESA<-auto.arima(y = IVAE_ESA)
summary(modelo_ESA)
## Series: IVAE_ESA
## ARIMA(2,0,0)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 ar2 sma1 drift
## 0.9235 -0.1436 -0.8361 0.1618
## s.e. 0.0802 0.0807 0.0825 0.0199
##
## sigma^2 = 5.79: log likelihood = -354.8
## AIC=719.59 AICc=720 BIC=734.71
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.06329184 2.2858 1.545284 0.03154945 1.523289 0.4028524
## ACF1
## Training set -0.003845263
pronosticos_ESA<-forecast(modelo_ESA,h = 6)
autoplot(pronosticos_ESA)+xlab("Años")+ylab("indice")+theme_bw()
autoplot(pronosticos_ESA$x,series = "IVAE")+autolayer(pronosticos_ESA$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
Serie ampliada
IVAE_ESA_Amp<-ts(as.numeric(rbind(as.matrix(pronosticos_ESA$x),as.matrix(pronosticos_ESA$mean))),start = c(2009,1),frequency = 12)
print(IVAE_ESA_Amp)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 86.7300 80.8500 87.1900 83.9200 91.4200 93.4600 86.3900 86.7200
## 2010 85.5600 84.6900 90.9000 85.9400 94.3300 92.2300 87.1800 90.2500
## 2011 90.2700 86.7300 94.3200 90.7900 98.5000 97.5900 92.1600 94.2200
## 2012 92.6500 91.2000 98.4600 91.2300 102.8300 102.8400 93.6100 98.2100
## 2013 95.6700 90.7700 96.1200 96.3400 103.0800 101.5800 96.4200 98.9600
## 2014 98.7000 94.7000 101.3000 97.1200 103.8600 104.7300 98.4800 98.6000
## 2015 98.8700 94.8200 103.1500 98.7500 105.6500 105.4500 101.6700 101.0600
## 2016 99.2500 97.7600 102.5800 103.4300 107.7600 110.7100 104.0100 106.2400
## 2017 101.4100 98.9700 108.4400 101.4000 110.8500 113.6300 105.5100 107.8800
## 2018 105.1700 102.5300 108.3900 107.9300 112.4600 113.5500 108.8000 111.9400
## 2019 108.1000 106.4100 113.0200 109.9500 114.9500 114.8600 111.2400 113.2800
## 2020 109.4900 109.2700 104.0400 87.3600 89.3300 96.0500 96.9500 103.3400
## 2021 106.8400 107.0400 114.5100 109.7200 115.4300 115.1900 112.1600 114.2300
## 2022 109.2500 110.2800 118.8500 111.1500 120.3300 118.2700 113.3600 116.3000
## 2023 113.6007 112.1216
## Sep Oct Nov Dec
## 2009 87.5700 85.2700 91.8600 99.6400
## 2010 89.0000 88.7400 93.1300 100.7400
## 2011 92.3300 89.0600 96.8600 103.9100
## 2012 93.9400 93.4900 99.6100 105.0500
## 2013 97.7400 96.2200 101.2400 108.3700
## 2014 98.2500 96.4300 100.6400 107.1900
## 2015 100.6400 100.4400 104.9000 109.8600
## 2016 104.8300 102.0400 106.5000 114.9800
## 2017 106.2100 103.2800 110.3900 117.5600
## 2018 107.5400 105.8100 112.1600 120.0300
## 2019 111.6600 108.3200 116.1000 122.0800
## 2020 106.7200 106.1200 110.7000 119.8600
## 2021 113.8200 109.7300 116.7000 123.6900
## 2022 115.3201 112.9454 118.8018 125.9326
## 2023
Descomposición de la Serie Temporal
FIT_ESA<-stl(IVAE_ESA_Amp,"periodic")
autoplot(FIT_ESA)+theme_classic()
TC_ESA<-FIT_ESA$time.series[,2]
print(TC_ESA)
## Jan Feb Mar Apr May Jun Jul
## 2009 87.44441 87.60681 87.76921 87.92794 88.08667 88.25008 88.41348
## 2010 89.40028 89.55340 89.70651 89.86944 90.03236 90.22289 90.41342
## 2011 92.44334 92.80161 93.15988 93.39982 93.63975 93.84970 94.05965
## 2012 95.62347 95.95655 96.28962 96.54669 96.80376 96.94365 97.08353
## 2013 97.37067 97.57465 97.77863 98.01141 98.24418 98.49901 98.75383
## 2014 99.97696 100.08692 100.19688 100.19388 100.19088 100.13145 100.07202
## 2015 100.61674 100.90043 101.18412 101.48393 101.78373 101.98809 102.19245
## 2016 103.31134 103.67683 104.04231 104.37187 104.70143 104.96083 105.22023
## 2017 105.98874 106.19442 106.40010 106.64005 106.87999 107.13555 107.39110
## 2018 108.53384 108.78914 109.04444 109.27664 109.50885 109.71924 109.92962
## 2019 111.20285 111.44804 111.69322 111.95644 112.21966 112.39504 112.57042
## 2020 106.56927 105.30931 104.04934 103.38688 102.72442 102.59403 102.46364
## 2021 110.00438 110.88175 111.75911 112.26149 112.76387 113.08083 113.39780
## 2022 114.86324 115.06235 115.26147 115.47510 115.68873 115.83923 115.98974
## 2023 116.75131 116.86242
## Aug Sep Oct Nov Dec
## 2009 88.59338 88.77329 88.95274 89.13219 89.26623
## 2010 90.66268 90.91194 91.25980 91.60766 92.02550
## 2011 94.26976 94.47988 94.72779 94.97570 95.29959
## 2012 97.10042 97.11730 97.14252 97.16774 97.26920
## 2013 99.00009 99.24636 99.44622 99.64608 99.81152
## 2014 100.05130 100.03057 100.12173 100.21288 100.41481
## 2015 102.28931 102.38617 102.54615 102.70613 103.00874
## 2016 105.37231 105.52438 105.61158 105.69878 105.84376
## 2017 107.59635 107.80160 107.96728 108.13297 108.33340
## 2018 110.15601 110.38239 110.58903 110.79568 110.99926
## 2019 112.33843 112.10644 110.95596 109.80548 108.18737
## 2020 103.18295 103.90226 105.45812 107.01397 108.50918
## 2021 113.63685 113.87590 114.12496 114.37401 114.61862
## 2022 116.12786 116.26598 116.39214 116.51830 116.63480
## 2023
Calculo de las Tasa
TC_ESA %>% as.numeric() %>% as.data.frame()->TC_ESA_df
names(TC_ESA_df)<-c("TC_ESA")
TC_ESA_df %>% mutate(T_1_1=(TC_ESA/dplyr::lag(TC_ESA,n=1)-1)*100,
T_1_12=(TC_ESA/dplyr::lag(TC_ESA, n=12)-1)*100,
T_12_12=(rollapply(TC_ESA,12,mean,align="right",
fill=NA)/rollapply(dplyr::lag(TC_ESA, n=12), 12,mean,align="right", fill=NA)-1)*100) %>%
mutate(T_1_12C=dplyr::lead(T_1_12, n=6),
T_12_12C=dplyr::lead(T_12_12, n=12)) %>%
ts(start = c(2009,1), frequency = 12)->Coyun_ESA
print(tail(Coyun_ESA, n=12))
## TC_ESA T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## Mar 2022 115.2615 0.17304748 3.133843 7.503904 2.098839 NA
## Apr 2022 115.4751 0.18534593 2.862610 7.009903 1.986578 NA
## May 2022 115.6887 0.18500303 2.593794 6.404204 1.874805 NA
## Jun 2022 115.8392 0.13009289 2.439316 5.758686 1.759034 NA
## Jul 2022 115.9897 0.12992386 2.285702 5.074966 1.643756 NA
## Aug 2022 116.1279 0.11907927 2.192074 4.432823 1.564430 NA
## Sep 2022 116.2660 0.11893764 2.098839 3.830585 NA NA
## Oct 2022 116.3921 0.10850962 1.986578 3.328714 NA NA
## Nov 2022 116.5183 0.10839200 1.874805 2.923816 NA NA
## Dec 2022 116.6348 0.09999144 1.759034 2.608495 NA NA
## Jan 2023 116.7513 0.09989156 1.643756 2.380675 NA NA
## Feb 2023 116.8624 0.09516974 1.564430 2.198828 NA NA
Gráfico de las Tasas (Centradas)
Coyun_ESA %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->Graficos_ESA
autoplot(Graficos_ESA)+theme_bw()
Coyun_ESA %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()
GUATEMALA
Datos
DATA_CA_y_PAN %>% select("Guatemala") %>% as.data.frame() %>% na.omit()->IVAE_GTM
IVAE_GTM<- ts(data = IVAE_GTM, start = c(2009,1), frequency = 12)
print(IVAE_GTM)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2009 86.65 84.95 90.17 87.79 85.69 83.92 87.09 85.90 84.65 87.06
## 2010 88.43 87.09 94.14 89.68 88.28 87.49 88.03 87.35 86.92 88.69
## 2011 92.16 91.28 96.96 93.60 92.20 91.60 92.65 92.61 92.08 91.78
## 2012 95.05 94.95 101.10 95.13 95.58 94.13 94.97 95.31 94.02 96.32
## 2013 99.07 98.81 101.72 101.20 99.50 96.72 98.64 98.67 97.72 99.48
## 2014 102.75 102.57 106.76 104.80 104.40 101.05 103.78 102.20 101.78 103.90
## 2015 107.76 107.15 111.74 107.66 106.67 105.63 108.72 107.53 106.64 108.45
## 2016 109.74 109.44 112.96 112.29 111.12 108.40 109.35 110.41 109.80 110.43
## 2017 115.42 114.30 118.07 114.70 113.72 111.63 113.82 113.93 112.07 113.68
## 2018 117.75 117.77 121.77 119.59 118.71 116.35 118.22 118.04 115.42 117.98
## 2019 122.08 122.76 126.05 123.95 123.67 120.45 122.93 121.94 120.78 122.99
## 2020 127.01 125.51 121.38 112.73 111.49 111.55 118.50 120.60 121.73 125.20
## 2021 128.88 128.61 133.29 130.06 130.01 127.53 131.22 130.13 128.77 130.62
## 2022 134.95 134.14 139.23 135.83 135.53 132.00 135.09 136.00
## Nov Dec
## 2009 87.94 95.19
## 2010 91.35 98.92
## 2011 95.86 101.43
## 2012 98.92 104.11
## 2013 102.16 106.30
## 2014 107.09 112.27
## 2015 111.44 115.24
## 2016 114.99 120.63
## 2017 116.91 122.56
## 2018 121.04 125.20
## 2019 126.94 130.45
## 2020 128.05 135.04
## 2021 135.34 140.77
## 2022
autoplot(IVAE_GTM,xlab = "años",ylab = "Indice",main = "IVAE-Guatemala total, periodo 2009-2022 (Agosto)")+theme_classic()
Proyección a Seis meses
modelo_GTM<-auto.arima(y = IVAE_GTM)
summary(modelo_GTM)
## Series: IVAE_GTM
## ARIMA(1,0,1)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 ma1 sma1 drift
## 0.7713 0.2210 -0.7828 0.3054
## s.e. 0.0643 0.1038 0.0736 0.0151
##
## sigma^2 = 1.961: log likelihood = -271.08
## AIC=552.16 AICc=552.57 BIC=567.28
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.02077232 1.330162 0.8846027 -0.03576932 0.8034655 0.2047349
## ACF1
## Training set 0.003034942
pronosticos_GTM<-forecast(modelo_GTM,h = 6)
autoplot(pronosticos_GTM)+xlab("Años")+ylab("indice")+theme_bw()
autoplot(pronosticos_GTM$x,series = "IVAE")+autolayer(pronosticos_GTM$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
Serie ampliada
IVAE_GTM_Amp<-ts(as.numeric(rbind(as.matrix(pronosticos_GTM$x),as.matrix(pronosticos_GTM$mean))),start = c(2009,1),frequency = 12)
print(IVAE_GTM_Amp)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 86.6500 84.9500 90.1700 87.7900 85.6900 83.9200 87.0900 85.9000
## 2010 88.4300 87.0900 94.1400 89.6800 88.2800 87.4900 88.0300 87.3500
## 2011 92.1600 91.2800 96.9600 93.6000 92.2000 91.6000 92.6500 92.6100
## 2012 95.0500 94.9500 101.1000 95.1300 95.5800 94.1300 94.9700 95.3100
## 2013 99.0700 98.8100 101.7200 101.2000 99.5000 96.7200 98.6400 98.6700
## 2014 102.7500 102.5700 106.7600 104.8000 104.4000 101.0500 103.7800 102.2000
## 2015 107.7600 107.1500 111.7400 107.6600 106.6700 105.6300 108.7200 107.5300
## 2016 109.7400 109.4400 112.9600 112.2900 111.1200 108.4000 109.3500 110.4100
## 2017 115.4200 114.3000 118.0700 114.7000 113.7200 111.6300 113.8200 113.9300
## 2018 117.7500 117.7700 121.7700 119.5900 118.7100 116.3500 118.2200 118.0400
## 2019 122.0800 122.7600 126.0500 123.9500 123.6700 120.4500 122.9300 121.9400
## 2020 127.0100 125.5100 121.3800 112.7300 111.4900 111.5500 118.5000 120.6000
## 2021 128.8800 128.6100 133.2900 130.0600 130.0100 127.5300 131.2200 130.1300
## 2022 134.9500 134.1400 139.2300 135.8300 135.5300 132.0000 135.0900 136.0000
## 2023 138.2722 137.4518
## Sep Oct Nov Dec
## 2009 84.6500 87.0600 87.9400 95.1900
## 2010 86.9200 88.6900 91.3500 98.9200
## 2011 92.0800 91.7800 95.8600 101.4300
## 2012 94.0200 96.3200 98.9200 104.1100
## 2013 97.7200 99.4800 102.1600 106.3000
## 2014 101.7800 103.9000 107.0900 112.2700
## 2015 106.6400 108.4500 111.4400 115.2400
## 2016 109.8000 110.4300 114.9900 120.6300
## 2017 112.0700 113.6800 116.9100 122.5600
## 2018 115.4200 117.9800 121.0400 125.2000
## 2019 120.7800 122.9900 126.9400 130.4500
## 2020 121.7300 125.2000 128.0500 135.0400
## 2021 128.7700 130.6200 135.3400 140.7700
## 2022 134.5206 135.8710 138.8395 143.6604
## 2023
Descomposición de la Serie Temporal
FIT_GTM<-stl(IVAE_GTM_Amp,"periodic")
autoplot(FIT_GTM)+theme_classic()
TC_GTM<-FIT_GTM$time.series[,2]
print(TC_GTM)
## Jan Feb Mar Apr May Jun Jul
## 2009 86.09008 86.30302 86.51596 86.71982 86.92368 87.12729 87.33089
## 2010 88.53281 88.72066 88.90851 89.12271 89.33692 89.59679 89.85667
## 2011 91.78915 92.20768 92.62621 92.98308 93.33995 93.61345 93.88695
## 2012 95.24916 95.49768 95.74620 96.02047 96.29475 96.56285 96.83096
## 2013 98.53034 98.84162 99.15290 99.43610 99.71930 99.97623 100.23316
## 2014 102.25034 102.65769 103.06505 103.46722 103.86939 104.26684 104.66429
## 2015 106.68884 107.10688 107.52491 107.90992 108.29492 108.56914 108.84337
## 2016 110.11376 110.33485 110.55594 110.83925 111.12257 111.49962 111.87668
## 2017 113.85089 114.13063 114.41037 114.61709 114.82382 115.00432 115.18482
## 2018 117.20653 117.60276 117.99900 118.34025 118.68149 118.96865 119.25582
## 2019 121.38350 121.83398 122.28446 122.73258 123.18071 123.55110 123.92150
## 2020 121.49722 121.07874 120.66026 120.62439 120.58853 120.91143 121.23433
## 2021 127.78876 128.59284 129.39692 129.96385 130.53078 131.02998 131.52918
## 2022 134.24461 134.67660 135.10859 135.50207 135.89556 136.14844 136.40131
## 2023 137.77995 137.98816
## Aug Sep Oct Nov Dec
## 2009 87.53974 87.74860 87.93975 88.13090 88.33186
## 2010 90.13258 90.40849 90.71422 91.01994 91.40454
## 2011 94.12413 94.36131 94.57943 94.79755 95.02335
## 2012 97.08317 97.33538 97.61995 97.90451 98.21743
## 2013 100.50400 100.77484 101.11441 101.45398 101.85216
## 2014 105.01068 105.35707 105.66390 105.97072 106.32978
## 2015 109.03790 109.23242 109.44991 109.66740 109.89058
## 2016 112.25606 112.63543 112.94838 113.26134 113.55611
## 2017 115.43138 115.67793 116.03206 116.38619 116.79636
## 2018 119.54944 119.84307 120.19695 120.55084 120.96717
## 2019 123.95291 123.98431 123.45808 122.93185 122.21453
## 2020 122.05405 122.87377 124.14059 125.40742 126.59809
## 2021 132.00580 132.48241 132.93393 133.38544 133.81503
## 2022 136.63967 136.87802 137.10971 137.34140 137.56068
## 2023
Calculo de las Tasa
TC_GTM %>% as.numeric() %>% as.data.frame()->TC_GTM_df
names(TC_GTM_df)<-c("TC_GTM")
TC_GTM_df %>% mutate(T_1_1=(TC_GTM/dplyr::lag(TC_GTM,n=1)-1)*100,
T_1_12=(TC_GTM/dplyr::lag(TC_GTM, n=12)-1)*100,
T_12_12=(rollapply(TC_GTM,12,mean,align="right",
fill=NA)/rollapply(dplyr::lag(TC_GTM, n=12), 12,mean,align="right", fill=NA)-1)*100) %>%
mutate(T_1_12C=dplyr::lead(T_1_12, n=6),
T_12_12C=dplyr::lead(T_12_12, n=12)) %>%
ts(start = c(2009,1), frequency = 12)->Coyun_GTM
print(tail(Coyun_GTM, n=12))
## TC_GTM T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## Mar 2022 135.1086 0.3207585 4.414071 6.810785 3.317881 NA
## Apr 2022 135.5021 0.2912372 4.261359 6.514875 3.141249 NA
## May 2022 135.8956 0.2903915 4.109973 6.168720 2.965813 NA
## Jun 2022 136.1484 0.1860811 3.906326 5.798607 2.799125 NA
## Jul 2022 136.4013 0.1857355 3.704225 5.405140 2.633505 NA
## Aug 2022 136.6397 0.1747457 3.510356 5.024860 2.458894 NA
## Sep 2022 136.8780 0.1744409 3.317881 4.657259 NA NA
## Oct 2022 137.1097 0.1692665 3.141249 4.334650 NA NA
## Nov 2022 137.3414 0.1689805 2.965813 4.055773 NA NA
## Dec 2022 137.5607 0.1596577 2.799125 3.816555 NA NA
## Jan 2023 137.7800 0.1594032 2.633505 3.616106 NA NA
## Feb 2023 137.9882 0.1511127 2.458894 3.427442 NA NA
Gráfico de las Tasas (Centradas)
Coyun_GTM %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->Graficos_GTM
autoplot(Graficos_GTM)+theme_bw()
Coyun_GTM %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()
HONDURAS
Datos
DATA_CA_y_PAN %>% select("Honduras") %>% as.data.frame() %>% na.omit()->IVAE_HND
IVAE_HND<- ts(data = IVAE_HND, start = c(2009,1), frequency = 12)
print(IVAE_HND)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2009 157.26 159.33 169.91 156.18 164.17 163.04 155.42 159.89 157.82 166.33
## 2010 165.28 166.91 179.91 165.46 173.89 171.00 162.53 166.65 175.18 172.00
## 2011 176.96 179.46 190.71 175.18 184.30 182.33 175.83 185.67 182.03 185.82
## 2012 181.51 189.25 202.52 183.81 193.45 192.28 185.89 193.61 188.79 199.97
## 2013 189.68 192.66 196.37 195.49 199.00 194.38 190.45 196.66 191.32 201.79
## 2014 194.20 197.58 205.41 197.36 207.03 198.09 194.18 199.21 197.73 205.50
## 2015 200.82 202.02 214.06 206.39 206.66 206.13 201.94 207.78 204.91 213.81
## 2016 207.87 210.56 220.51 211.07 214.45 216.00 205.61 215.98 212.31 220.76
## 2017 219.37 221.50 233.93 218.03 225.53 225.90 216.75 229.08 226.26 232.75
## 2018 228.97 228.12 237.11 227.12 234.88 234.03 225.04 238.66 232.55 244.93
## 2019 235.30 235.08 246.40 234.80 241.51 235.46 238.02 244.65 239.69 252.72
## 2020 242.49 241.65 218.27 186.88 189.07 208.71 209.30 225.80 230.24 249.34
## 2021 229.97 236.28 251.05 235.96 242.36 247.40 239.81 256.77 246.87 265.45
## 2022 247.27 246.62 263.70 248.77 254.73 256.23 246.20 272.14
## Nov Dec
## 2009 163.97 176.16
## 2010 175.48 186.89
## 2011 188.18 198.66
## 2012 199.48 203.10
## 2013 201.54 213.57
## 2014 203.26 221.72
## 2015 214.73 231.40
## 2016 227.59 245.58
## 2017 235.80 251.23
## 2018 245.16 262.48
## 2019 250.26 273.80
## 2020 218.89 258.08
## 2021 264.73 279.05
## 2022
autoplot(IVAE_HND,xlab = "años",ylab = "Indice",main = "IVAE-Honduras total, periodo 2009-2022 (Agosto)")+theme_classic()
Proyección a Seis meses
modelo_HND<-auto.arima(y = IVAE_HND)
summary(modelo_HND)
## Series: IVAE_HND
## ARIMA(1,0,0)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 sma1 drift
## 0.7873 -0.6826 0.5907
## s.e. 0.0505 0.0664 0.0756
##
## sigma^2 = 40.52: log likelihood = -499.71
## AIC=1007.42 AICc=1007.69 BIC=1019.51
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.1146119 6.067638 3.641689 0.003617344 1.685864 0.3224686
## ACF1
## Training set -0.03097548
pronosticos_HND<-forecast(modelo_HND,h = 6)
autoplot(pronosticos_HND)+xlab("Años")+ylab("indice")+theme_bw()
autoplot(pronosticos_HND$x,series = "IVAE")+autolayer(pronosticos_HND$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
Serie ampliada
IVAE_HND_Amp<-ts(as.numeric(rbind(as.matrix(pronosticos_HND$x),as.matrix(pronosticos_HND$mean))),start = c(2009,1),frequency = 12)
print(IVAE_HND_Amp)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 157.2600 159.3300 169.9100 156.1800 164.1700 163.0400 155.4200 159.8900
## 2010 165.2800 166.9100 179.9100 165.4600 173.8900 171.0000 162.5300 166.6500
## 2011 176.9600 179.4600 190.7100 175.1800 184.3000 182.3300 175.8300 185.6700
## 2012 181.5100 189.2500 202.5200 183.8100 193.4500 192.2800 185.8900 193.6100
## 2013 189.6800 192.6600 196.3700 195.4900 199.0000 194.3800 190.4500 196.6600
## 2014 194.2000 197.5800 205.4100 197.3600 207.0300 198.0900 194.1800 199.2100
## 2015 200.8200 202.0200 214.0600 206.3900 206.6600 206.1300 201.9400 207.7800
## 2016 207.8700 210.5600 220.5100 211.0700 214.4500 216.0000 205.6100 215.9800
## 2017 219.3700 221.5000 233.9300 218.0300 225.5300 225.9000 216.7500 229.0800
## 2018 228.9700 228.1200 237.1100 227.1200 234.8800 234.0300 225.0400 238.6600
## 2019 235.3000 235.0800 246.4000 234.8000 241.5100 235.4600 238.0200 244.6500
## 2020 242.4900 241.6500 218.2700 186.8800 189.0700 208.7100 209.3000 225.8000
## 2021 229.9700 236.2800 251.0500 235.9600 242.3600 247.4000 239.8100 256.7700
## 2022 247.2700 246.6200 263.7000 248.7700 254.7300 256.2300 246.2000 272.1400
## 2023 260.0353 260.4971
## Sep Oct Nov Dec
## 2009 157.8200 166.3300 163.9700 176.1600
## 2010 175.1800 172.0000 175.4800 186.8900
## 2011 182.0300 185.8200 188.1800 198.6600
## 2012 188.7900 199.9700 199.4800 203.1000
## 2013 191.3200 201.7900 201.5400 213.5700
## 2014 197.7300 205.5000 203.2600 221.7200
## 2015 204.9100 213.8100 214.7300 231.4000
## 2016 212.3100 220.7600 227.5900 245.5800
## 2017 226.2600 232.7500 235.8000 251.2300
## 2018 232.5500 244.9300 245.1600 262.4800
## 2019 239.6900 252.7200 250.2600 273.8000
## 2020 230.2400 249.3400 218.8900 258.0800
## 2021 246.8700 265.4500 264.7300 279.0500
## 2022 265.0962 277.6778 269.2452 289.4112
## 2023
Descomposición de la Serie Temporal
FIT_HND<-stl(IVAE_HND_Amp,"periodic")
autoplot(FIT_HND)+theme_classic()
TC_HND<-FIT_HND$time.series[,2]
print(TC_HND)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 162.8929 162.9561 163.0194 163.1303 163.2413 163.4432 163.6452 163.9242
## 2010 167.0260 168.0565 169.0871 170.0043 170.9216 171.6407 172.3599 173.0451
## 2011 177.8868 179.1969 180.5071 181.5959 182.6848 183.3904 184.0961 184.6435
## 2012 188.4348 189.4048 190.3749 191.3038 192.2328 192.7783 193.3238 193.4674
## 2013 194.8125 195.2254 195.6384 196.0381 196.4378 196.8346 197.2315 197.5486
## 2014 199.5783 200.0938 200.6092 201.0205 201.4318 201.7751 202.1184 202.4675
## 2015 205.0900 205.8980 206.7060 207.4727 208.2393 208.8819 209.5246 210.0341
## 2016 213.0173 213.7139 214.4105 215.1539 215.8972 216.7680 217.6388 218.5649
## 2017 223.3707 224.4099 225.4492 226.2883 227.1275 227.7553 228.3831 228.9430
## 2018 232.1604 232.9145 233.6685 234.4485 235.2286 236.0122 236.7959 237.4830
## 2019 240.4423 241.0225 241.6028 242.1963 242.7897 243.4264 244.0631 243.7318
## 2020 232.0460 229.5277 227.0094 225.2767 223.5440 222.7540 221.9639 223.2827
## 2021 237.1844 239.4830 241.7816 243.9924 246.2031 248.2298 250.2565 251.5898
## 2022 256.2256 257.0716 257.9175 258.9447 259.9720 261.0707 262.1694 263.1515
## 2023 268.2480 269.2274
## Sep Oct Nov Dec
## 2009 164.2033 164.7060 165.2087 166.1173
## 2010 173.7303 174.6313 175.5324 176.7096
## 2011 185.1909 185.9133 186.6357 187.5352
## 2012 193.6110 193.8109 194.0107 194.4116
## 2013 197.8658 198.2317 198.5976 199.0879
## 2014 202.8165 203.2806 203.7446 204.4173
## 2015 210.5437 211.0889 211.6342 212.3257
## 2016 219.4910 220.4128 221.3345 222.3526
## 2017 229.5029 230.1126 230.7224 231.4414
## 2018 238.1702 238.7462 239.3221 239.8822
## 2019 243.4005 240.9754 238.5503 235.2982
## 2020 224.6016 227.8072 231.0128 234.0986
## 2021 252.9232 253.8282 254.7332 255.4794
## 2022 264.1337 265.1653 266.1970 267.2225
## 2023
Calculo de las Tasa
TC_HND %>% as.numeric() %>% as.data.frame()->TC_HND_df
names(TC_HND_df)<-c("TC_HND")
TC_HND_df %>% mutate(T_1_1=(TC_HND/dplyr::lag(TC_HND,n=1)-1)*100,
T_1_12=(TC_HND/dplyr::lag(TC_HND, n=12)-1)*100,
T_12_12=(rollapply(TC_HND,12,mean,align="right",
fill=NA)/rollapply(dplyr::lag(TC_HND, n=12), 12,mean,align="right", fill=NA)-1)*100) %>%
mutate(T_1_12C=dplyr::lead(T_1_12, n=6),
T_12_12C=dplyr::lead(T_12_12, n=12)) %>%
ts(start = c(2009,1), frequency = 12)->Coyun_HND
print(tail(Coyun_HND, n=12))
## TC_HND T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## Mar 2022 257.9175 0.3290682 6.673751 10.013832 4.432366 NA
## Apr 2022 258.9447 0.3982731 6.128209 9.810425 4.466462 NA
## May 2022 259.9720 0.3966932 5.592463 9.412695 4.500315 NA
## Jun 2022 261.0707 0.4226321 5.172975 8.879544 4.596477 NA
## Jul 2022 262.1694 0.4208535 4.760281 8.216186 4.692079 NA
## Aug 2022 263.1515 0.3746166 4.595455 7.553127 4.728556 NA
## Sep 2022 264.1337 0.3732185 4.432366 6.890368 NA NA
## Oct 2022 265.1653 0.3905831 4.466462 6.328132 NA NA
## Nov 2022 266.1970 0.3890635 4.500315 5.862449 NA NA
## Dec 2022 267.2225 0.3852338 4.596477 5.496442 NA NA
## Jan 2023 268.2480 0.3837555 4.692079 5.227119 NA NA
## Feb 2023 269.2274 0.3651127 4.728556 5.016246 NA NA
Gráfico de las Tasas (Centradas)
Coyun_HND %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->Graficos_HND
autoplot(Graficos_HND)+theme_bw()
Coyun_HND %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()
NICARAGUA
Datos
DATA_CA_y_PAN %>% select("Nicaragua") %>% as.data.frame() %>% na.omit()->IVAE_NIC
IVAE_NIC<- ts(data = IVAE_HND, start = c(2009,1), frequency = 12)
print(IVAE_NIC)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2009 157.26 159.33 169.91 156.18 164.17 163.04 155.42 159.89 157.82 166.33
## 2010 165.28 166.91 179.91 165.46 173.89 171.00 162.53 166.65 175.18 172.00
## 2011 176.96 179.46 190.71 175.18 184.30 182.33 175.83 185.67 182.03 185.82
## 2012 181.51 189.25 202.52 183.81 193.45 192.28 185.89 193.61 188.79 199.97
## 2013 189.68 192.66 196.37 195.49 199.00 194.38 190.45 196.66 191.32 201.79
## 2014 194.20 197.58 205.41 197.36 207.03 198.09 194.18 199.21 197.73 205.50
## 2015 200.82 202.02 214.06 206.39 206.66 206.13 201.94 207.78 204.91 213.81
## 2016 207.87 210.56 220.51 211.07 214.45 216.00 205.61 215.98 212.31 220.76
## 2017 219.37 221.50 233.93 218.03 225.53 225.90 216.75 229.08 226.26 232.75
## 2018 228.97 228.12 237.11 227.12 234.88 234.03 225.04 238.66 232.55 244.93
## 2019 235.30 235.08 246.40 234.80 241.51 235.46 238.02 244.65 239.69 252.72
## 2020 242.49 241.65 218.27 186.88 189.07 208.71 209.30 225.80 230.24 249.34
## 2021 229.97 236.28 251.05 235.96 242.36 247.40 239.81 256.77 246.87 265.45
## 2022 247.27 246.62 263.70 248.77 254.73 256.23 246.20 272.14
## Nov Dec
## 2009 163.97 176.16
## 2010 175.48 186.89
## 2011 188.18 198.66
## 2012 199.48 203.10
## 2013 201.54 213.57
## 2014 203.26 221.72
## 2015 214.73 231.40
## 2016 227.59 245.58
## 2017 235.80 251.23
## 2018 245.16 262.48
## 2019 250.26 273.80
## 2020 218.89 258.08
## 2021 264.73 279.05
## 2022
autoplot(IVAE_NIC,xlab = "años",ylab = "Indice",main = "IVAE-Nicaragua total, periodo 2009-2022 (Agosto)")+theme_classic()
Proyección a Seis meses
modelo_NIC<-auto.arima(y = IVAE_NIC)
summary(modelo_NIC)
## Series: IVAE_NIC
## ARIMA(1,0,0)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 sma1 drift
## 0.7873 -0.6826 0.5907
## s.e. 0.0505 0.0664 0.0756
##
## sigma^2 = 40.52: log likelihood = -499.71
## AIC=1007.42 AICc=1007.69 BIC=1019.51
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.1146119 6.067638 3.641689 0.003617344 1.685864 0.3224686
## ACF1
## Training set -0.03097548
pronosticos_NIC<-forecast(modelo_NIC,h = 6)
autoplot(pronosticos_NIC)+xlab("Años")+ylab("indice")+theme_bw()
autoplot(pronosticos_NIC$x,series = "IVAE")+autolayer(pronosticos_NIC$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
Serie ampliada
IVAE_NIC_Amp<-ts(as.numeric(rbind(as.matrix(pronosticos_NIC$x),as.matrix(pronosticos_NIC$mean))),start = c(2009,1),frequency = 12)
print(IVAE_NIC_Amp)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 157.2600 159.3300 169.9100 156.1800 164.1700 163.0400 155.4200 159.8900
## 2010 165.2800 166.9100 179.9100 165.4600 173.8900 171.0000 162.5300 166.6500
## 2011 176.9600 179.4600 190.7100 175.1800 184.3000 182.3300 175.8300 185.6700
## 2012 181.5100 189.2500 202.5200 183.8100 193.4500 192.2800 185.8900 193.6100
## 2013 189.6800 192.6600 196.3700 195.4900 199.0000 194.3800 190.4500 196.6600
## 2014 194.2000 197.5800 205.4100 197.3600 207.0300 198.0900 194.1800 199.2100
## 2015 200.8200 202.0200 214.0600 206.3900 206.6600 206.1300 201.9400 207.7800
## 2016 207.8700 210.5600 220.5100 211.0700 214.4500 216.0000 205.6100 215.9800
## 2017 219.3700 221.5000 233.9300 218.0300 225.5300 225.9000 216.7500 229.0800
## 2018 228.9700 228.1200 237.1100 227.1200 234.8800 234.0300 225.0400 238.6600
## 2019 235.3000 235.0800 246.4000 234.8000 241.5100 235.4600 238.0200 244.6500
## 2020 242.4900 241.6500 218.2700 186.8800 189.0700 208.7100 209.3000 225.8000
## 2021 229.9700 236.2800 251.0500 235.9600 242.3600 247.4000 239.8100 256.7700
## 2022 247.2700 246.6200 263.7000 248.7700 254.7300 256.2300 246.2000 272.1400
## 2023 260.0353 260.4971
## Sep Oct Nov Dec
## 2009 157.8200 166.3300 163.9700 176.1600
## 2010 175.1800 172.0000 175.4800 186.8900
## 2011 182.0300 185.8200 188.1800 198.6600
## 2012 188.7900 199.9700 199.4800 203.1000
## 2013 191.3200 201.7900 201.5400 213.5700
## 2014 197.7300 205.5000 203.2600 221.7200
## 2015 204.9100 213.8100 214.7300 231.4000
## 2016 212.3100 220.7600 227.5900 245.5800
## 2017 226.2600 232.7500 235.8000 251.2300
## 2018 232.5500 244.9300 245.1600 262.4800
## 2019 239.6900 252.7200 250.2600 273.8000
## 2020 230.2400 249.3400 218.8900 258.0800
## 2021 246.8700 265.4500 264.7300 279.0500
## 2022 265.0962 277.6778 269.2452 289.4112
## 2023
Descomposición de la Serie Temporal
FIT_NIC<-stl(IVAE_NIC_Amp,"periodic")
autoplot(FIT_NIC)+theme_classic()
TC_NIC<-FIT_NIC$time.series[,2]
print(TC_NIC)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 162.8929 162.9561 163.0194 163.1303 163.2413 163.4432 163.6452 163.9242
## 2010 167.0260 168.0565 169.0871 170.0043 170.9216 171.6407 172.3599 173.0451
## 2011 177.8868 179.1969 180.5071 181.5959 182.6848 183.3904 184.0961 184.6435
## 2012 188.4348 189.4048 190.3749 191.3038 192.2328 192.7783 193.3238 193.4674
## 2013 194.8125 195.2254 195.6384 196.0381 196.4378 196.8346 197.2315 197.5486
## 2014 199.5783 200.0938 200.6092 201.0205 201.4318 201.7751 202.1184 202.4675
## 2015 205.0900 205.8980 206.7060 207.4727 208.2393 208.8819 209.5246 210.0341
## 2016 213.0173 213.7139 214.4105 215.1539 215.8972 216.7680 217.6388 218.5649
## 2017 223.3707 224.4099 225.4492 226.2883 227.1275 227.7553 228.3831 228.9430
## 2018 232.1604 232.9145 233.6685 234.4485 235.2286 236.0122 236.7959 237.4830
## 2019 240.4423 241.0225 241.6028 242.1963 242.7897 243.4264 244.0631 243.7318
## 2020 232.0460 229.5277 227.0094 225.2767 223.5440 222.7540 221.9639 223.2827
## 2021 237.1844 239.4830 241.7816 243.9924 246.2031 248.2298 250.2565 251.5898
## 2022 256.2256 257.0716 257.9175 258.9447 259.9720 261.0707 262.1694 263.1515
## 2023 268.2480 269.2274
## Sep Oct Nov Dec
## 2009 164.2033 164.7060 165.2087 166.1173
## 2010 173.7303 174.6313 175.5324 176.7096
## 2011 185.1909 185.9133 186.6357 187.5352
## 2012 193.6110 193.8109 194.0107 194.4116
## 2013 197.8658 198.2317 198.5976 199.0879
## 2014 202.8165 203.2806 203.7446 204.4173
## 2015 210.5437 211.0889 211.6342 212.3257
## 2016 219.4910 220.4128 221.3345 222.3526
## 2017 229.5029 230.1126 230.7224 231.4414
## 2018 238.1702 238.7462 239.3221 239.8822
## 2019 243.4005 240.9754 238.5503 235.2982
## 2020 224.6016 227.8072 231.0128 234.0986
## 2021 252.9232 253.8282 254.7332 255.4794
## 2022 264.1337 265.1653 266.1970 267.2225
## 2023
Calculo de las Tasa
TC_NIC %>% as.numeric() %>% as.data.frame()->TC_NIC_df
names(TC_NIC_df)<-c("TC_NIC")
TC_NIC_df %>% mutate(T_1_1=(TC_NIC/dplyr::lag(TC_NIC,n=1)-1)*100,
T_1_12=(TC_NIC/dplyr::lag(TC_NIC, n=12)-1)*100,
T_12_12=(rollapply(TC_NIC,12,mean,align="right",
fill=NA)/rollapply(dplyr::lag(TC_NIC, n=12), 12,mean,align="right", fill=NA)-1)*100) %>%
mutate(T_1_12C=dplyr::lead(T_1_12, n=6),
T_12_12C=dplyr::lead(T_12_12, n=12)) %>%
ts(start = c(2009,1), frequency = 12)->Coyun_NIC
print(tail(Coyun_NIC, n=12))
## TC_NIC T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## Mar 2022 257.9175 0.3290682 6.673751 10.013832 4.432366 NA
## Apr 2022 258.9447 0.3982731 6.128209 9.810425 4.466462 NA
## May 2022 259.9720 0.3966932 5.592463 9.412695 4.500315 NA
## Jun 2022 261.0707 0.4226321 5.172975 8.879544 4.596477 NA
## Jul 2022 262.1694 0.4208535 4.760281 8.216186 4.692079 NA
## Aug 2022 263.1515 0.3746166 4.595455 7.553127 4.728556 NA
## Sep 2022 264.1337 0.3732185 4.432366 6.890368 NA NA
## Oct 2022 265.1653 0.3905831 4.466462 6.328132 NA NA
## Nov 2022 266.1970 0.3890635 4.500315 5.862449 NA NA
## Dec 2022 267.2225 0.3852338 4.596477 5.496442 NA NA
## Jan 2023 268.2480 0.3837555 4.692079 5.227119 NA NA
## Feb 2023 269.2274 0.3651127 4.728556 5.016246 NA NA
Gráfico de las Tasas (Centradas)
Coyun_NIC %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->Graficos_NIC
autoplot(Graficos_NIC)+theme_bw()
Coyun_NIC %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()
PANAMÁ
Datos
DATA_CA_y_PAN %>% select("Panamá") %>% as.data.frame() %>% na.omit()->IVAE_PAN
IVAE_PAN<- ts(data = IVAE_PAN, start = c(2009,1), frequency = 12)
print(IVAE_PAN)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2009 195.71 189.71 204.64 188.06 193.65 199.60 188.20 190.96 195.45 204.89
## 2010 201.01 200.42 220.03 203.11 202.73 210.75 198.31 206.22 205.20 213.91
## 2011 212.36 218.50 228.61 218.93 227.12 226.92 210.41 225.69 222.92 233.74
## 2012 233.23 237.88 260.05 237.89 248.64 251.02 239.86 246.47 238.38 249.62
## 2013 253.29 254.04 276.60 262.60 268.38 269.16 256.27 265.08 259.72 280.51
## 2014 265.09 267.31 286.56 275.53 274.45 283.35 268.30 278.43 272.53 296.66
## 2015 281.48 276.75 307.31 280.85 281.06 294.76 279.85 290.40 283.40 310.57
## 2016 292.53 289.67 318.79 292.13 296.56 306.77 293.76 303.34 296.96 322.82
## 2017 305.84 307.27 344.01 309.60 316.06 324.68 304.97 318.19 310.13 335.94
## 2018 320.57 323.91 349.99 311.69 317.94 324.94 308.98 323.54 315.15 333.20
## 2019 332.39 332.73 353.65 319.17 325.72 332.45 325.11 336.06 332.01 346.53
## 2020 346.02 341.78 357.06 243.21 222.17 233.12 240.66 242.56 259.86 298.72
## 2021 304.59 322.77 354.90 307.26 314.67 309.91 306.48 318.85 317.91 344.08
## 2022 354.01 368.38 390.50 334.96 344.38 348.03 317.45 359.51
## Nov Dec
## 2009 185.82 190.56
## 2010 202.49 205.63
## 2011 226.59 231.09
## 2012 251.28 247.71
## 2013 272.24 270.52
## 2014 282.62 292.03
## 2015 295.47 300.89
## 2016 309.18 312.22
## 2017 322.13 324.80
## 2018 328.79 330.41
## 2019 341.09 341.27
## 2020 296.58 339.78
## 2021 332.46 395.90
## 2022
autoplot(IVAE_PAN,xlab = "años",ylab = "Indice",main = "IVAE-Panamá total, periodo 2009-2022 (Agosto)")+theme_classic()
Proyección a Seis meses
modelo_PAN<-auto.arima(y = IVAE_PAN)
summary(modelo_PAN)
## Series: IVAE_PAN
## ARIMA(1,0,2)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 ma1 ma2 sma1 drift
## 0.8591 -0.0045 0.1297 -0.4593 1.0167
## s.e. 0.0537 0.1011 0.0880 0.1003 0.3442
##
## sigma^2 = 135.4: log likelihood = -588.3
## AIC=1188.6 AICc=1189.18 BIC=1206.75
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.07830014 11.0181 5.999723 -0.06832539 2.122395 0.2800424
## ACF1
## Training set 0.000449099
pronosticos_PAN<-forecast(modelo_PAN,h = 6)
autoplot(pronosticos_PAN)+xlab("Años")+ylab("indice")+theme_bw()
autoplot(pronosticos_PAN$x,series = "IVAE")+autolayer(pronosticos_HND$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA ")
Serie ampliada
IVAE_PAN_Amp<-ts(as.numeric(rbind(as.matrix(pronosticos_PAN$x),as.matrix(pronosticos_PAN$mean))),start = c(2009,1),frequency = 12)
print(IVAE_PAN_Amp)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 195.7100 189.7100 204.6400 188.0600 193.6500 199.6000 188.2000 190.9600
## 2010 201.0100 200.4200 220.0300 203.1100 202.7300 210.7500 198.3100 206.2200
## 2011 212.3600 218.5000 228.6100 218.9300 227.1200 226.9200 210.4100 225.6900
## 2012 233.2300 237.8800 260.0500 237.8900 248.6400 251.0200 239.8600 246.4700
## 2013 253.2900 254.0400 276.6000 262.6000 268.3800 269.1600 256.2700 265.0800
## 2014 265.0900 267.3100 286.5600 275.5300 274.4500 283.3500 268.3000 278.4300
## 2015 281.4800 276.7500 307.3100 280.8500 281.0600 294.7600 279.8500 290.4000
## 2016 292.5300 289.6700 318.7900 292.1300 296.5600 306.7700 293.7600 303.3400
## 2017 305.8400 307.2700 344.0100 309.6000 316.0600 324.6800 304.9700 318.1900
## 2018 320.5700 323.9100 349.9900 311.6900 317.9400 324.9400 308.9800 323.5400
## 2019 332.3900 332.7300 353.6500 319.1700 325.7200 332.4500 325.1100 336.0600
## 2020 346.0200 341.7800 357.0600 243.2100 222.1700 233.1200 240.6600 242.5600
## 2021 304.5900 322.7700 354.9000 307.2600 314.6700 309.9100 306.4800 318.8500
## 2022 354.0100 368.3800 390.5000 334.9600 344.3800 348.0300 317.4500 359.5100
## 2023 377.5661 387.0177
## Sep Oct Nov Dec
## 2009 195.4500 204.8900 185.8200 190.5600
## 2010 205.2000 213.9100 202.4900 205.6300
## 2011 222.9200 233.7400 226.5900 231.0900
## 2012 238.3800 249.6200 251.2800 247.7100
## 2013 259.7200 280.5100 272.2400 270.5200
## 2014 272.5300 296.6600 282.6200 292.0300
## 2015 283.4000 310.5700 295.4700 300.8900
## 2016 296.9600 322.8200 309.1800 312.2200
## 2017 310.1300 335.9400 322.1300 324.8000
## 2018 315.1500 333.2000 328.7900 330.4100
## 2019 332.0100 346.5300 341.0900 341.2700
## 2020 259.8600 298.7200 296.5800 339.7800
## 2021 317.9100 344.0800 332.4600 395.9000
## 2022 353.6559 381.5419 369.4203 411.3908
## 2023
Descomposición de la Serie Temporal
FIT_PAN<-stl(IVAE_PAN_Amp,"periodic")
autoplot(FIT_PAN)+theme_classic()
TC_PAN<-FIT_PAN$time.series[,2]
print(TC_PAN)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 192.5034 193.0118 193.5203 193.9132 194.3061 194.6900 195.0739 195.5994
## 2010 199.1110 200.4670 201.8231 203.2822 204.7414 205.9343 207.1272 207.8752
## 2011 213.2980 215.1717 217.0455 219.1430 221.2405 223.1781 225.1157 226.6507
## 2012 235.3317 237.5384 239.7452 241.7110 243.6768 245.2283 246.7798 247.8806
## 2013 254.6459 256.7700 258.8942 261.1832 263.4723 265.1834 266.8945 267.6906
## 2014 271.1301 272.4223 273.7144 275.2501 276.7857 278.3031 279.8204 280.8491
## 2015 284.3681 285.4116 286.4550 287.7133 288.9715 290.1608 291.3500 292.2442
## 2016 296.4131 297.5917 298.7703 300.1445 301.5187 302.8779 304.2371 305.5342
## 2017 312.0120 313.3711 314.7301 316.1266 317.5231 318.7218 319.9204 320.6350
## 2018 322.1079 322.4591 322.8103 323.2576 323.7049 324.2519 324.7989 325.3460
## 2019 328.5264 329.7135 330.9007 332.1880 333.4753 334.5676 335.6599 335.6851
## 2020 313.0948 304.9626 296.8305 290.7894 284.7484 281.6624 278.5763 279.4113
## 2021 303.7174 308.8374 313.9574 317.6293 321.3012 324.7792 328.2573 332.0488
## 2022 348.3066 350.6275 352.9484 354.9738 356.9993 359.6712 362.3430 364.9106
## 2023 379.0465 382.2866
## Sep Oct Nov Dec
## 2009 196.1249 196.7355 197.3461 198.2286
## 2010 208.6231 209.5807 210.5384 211.9182
## 2011 228.1857 229.7888 231.3920 233.3618
## 2012 248.9814 250.1836 251.3857 253.0158
## 2013 268.4868 268.9704 269.4540 270.2921
## 2014 281.8779 282.4382 282.9985 283.6833
## 2015 293.1385 293.8552 294.5720 295.4925
## 2016 306.8314 308.0977 309.3641 310.6881
## 2017 321.3495 321.5466 321.7437 321.9258
## 2018 325.8932 326.4139 326.9346 327.7305
## 2019 335.7102 331.8942 328.0782 320.5865
## 2020 280.2463 285.3590 290.4716 297.0945
## 2021 335.8404 339.2806 342.7208 345.5137
## 2022 367.4782 370.2250 372.9719 376.0092
## 2023
Calculo de las Tasa
TC_PAN %>% as.numeric() %>% as.data.frame()->TC_PAN_df
names(TC_PAN_df)<-c("TC_PAN")
TC_PAN_df %>% mutate(T_1_1=(TC_PAN/dplyr::lag(TC_PAN,n=1)-1)*100,
T_1_12=(TC_PAN/dplyr::lag(TC_PAN, n=12)-1)*100,
T_12_12=(rollapply(TC_PAN,12,mean,align="right",
fill=NA)/rollapply(dplyr::lag(TC_PAN, n=12), 12,mean,align="right", fill=NA)-1)*100) %>%
mutate(T_1_12C=dplyr::lead(T_1_12, n=6),
T_12_12C=dplyr::lead(T_12_12, n=12)) %>%
ts(start = c(2009,1), frequency = 12)->Coyun_PAN
print(tail(Coyun_PAN, n=12))
## TC_PAN T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## Mar 2022 352.9484 0.6619302 12.419185 15.576603 9.420485 NA
## Apr 2022 354.9738 0.5738609 11.757267 15.756172 9.120609 NA
## May 2022 356.9993 0.5705865 11.110477 15.570294 8.826752 NA
## Jun 2022 359.6712 0.7484281 10.743272 15.155499 8.826136 NA
## Jul 2022 362.3430 0.7428683 10.383848 14.522132 8.825529 NA
## Aug 2022 364.9106 0.7086040 9.896666 13.781796 9.029259 NA
## Sep 2022 367.4782 0.7036182 9.420485 12.940730 NA NA
## Oct 2022 370.2250 0.7474859 9.120609 12.155172 NA NA
## Nov 2022 372.9719 0.7419400 8.826752 11.421780 NA NA
## Dec 2022 376.0092 0.8143509 8.826136 10.822528 NA NA
## Jan 2023 379.0465 0.8077728 8.825529 10.350756 NA NA
## Feb 2023 382.2866 0.8547957 9.029259 9.989364 NA NA
Gráfico de las Tasas (Centradas)
Coyun_PAN %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->Graficos_PAN
autoplot(Graficos_PAN)+theme_bw()
Coyun_PAN %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()
GRAFICO CONJUNTO DE CENTROAMÉRICA Y PANAMÁ
CRC<-Coyun_CRC %>% as.data.frame() %>% select(T_1_12C)
ESA<-Coyun_ESA %>% as.data.frame() %>% select(T_1_12C)
HND<-Coyun_HND %>% as.data.frame() %>% select(T_1_12C)
GTM<-Coyun_GTM %>% as.data.frame() %>% select(T_1_12C)
NIC<-Coyun_NIC %>% as.data.frame() %>% select(T_1_12C)
PAN<-Coyun_PAN %>% as.data.frame() %>% select(T_1_12C)
colnames(CRC)<-c("T_1_12C_CRC")
colnames(ESA)<-c("T_1_12C_ESA")
colnames(HND)<-c("T_1_12C_HND")
colnames(GTM)<-c("T_1_12C_GTM")
colnames(NIC)<-c("T_1_12C_NIC")
colnames(PAN)<-c("T_1_12C_PAN")
CA_Y_PAN<-as.data.frame(c(CRC,ESA,HND,GTM,NIC,PAN))
CA_Y_PAN %>% as.data.frame() %>% ts(start = c(2009,1), frequency = 12) %>% autoplot()
