Analisis de Coyuntura Economica
Kevin Adonay Lemus Perez
2022-11-29
#librerias
library(readxl)
library(magrittr)
library(tidyverse)
library(dplyr)
library(stats)
library(zoo)
library(ggplot2)
library(forecast)# base de datos a utilizar
DATA <- read_excel("C:/Users/pc/Desktop/Base_centro america y panama.xlsx",
col_types = c("skip", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric"), skip = 4)
colnames(DATA)<-c("Costa Rica", "El Salvador", "Guatemala", "Honduras", "Nicaragua", "Panamá")Guatemala
DATA %>% 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 87.79 85.69 83.92 87.09 85.90 84.65 87.06 87.94 95.19 88.43
## 2010 89.68 88.28 87.49 88.03 87.35 86.92 88.69 91.35 98.92 92.16
## 2011 93.60 92.20 91.60 92.65 92.61 92.08 91.78 95.86 101.43 95.05
## 2012 95.13 95.58 94.13 94.97 95.31 94.02 96.32 98.92 104.11 99.07
## 2013 101.20 99.50 96.72 98.64 98.67 97.72 99.48 102.16 106.30 102.75
## 2014 104.80 104.40 101.05 103.78 102.20 101.78 103.90 107.09 112.27 107.76
## 2015 107.66 106.67 105.63 108.72 107.53 106.64 108.45 111.44 115.24 109.74
## 2016 112.29 111.12 108.40 109.35 110.41 109.80 110.43 114.99 120.63 115.42
## 2017 114.70 113.72 111.63 113.82 113.93 112.07 113.68 116.91 122.56 117.75
## 2018 119.59 118.71 116.35 118.22 118.04 115.42 117.98 121.04 125.20 122.08
## 2019 123.95 123.67 120.45 122.93 121.94 120.78 122.99 126.94 130.45 127.01
## 2020 112.73 111.49 111.55 118.50 120.60 121.73 125.20 128.05 135.04 128.88
## 2021 130.06 130.01 127.53 131.22 130.13 128.77 130.62 135.34 140.77 134.95
## 2022 135.83 135.53 132.00 135.09 136.00
## Nov Dec
## 2009 87.09 94.14
## 2010 91.28 96.96
## 2011 94.95 101.10
## 2012 98.81 101.72
## 2013 102.57 106.76
## 2014 107.15 111.74
## 2015 109.44 112.96
## 2016 114.30 118.07
## 2017 117.77 121.77
## 2018 122.76 126.05
## 2019 125.51 121.38
## 2020 128.61 133.29
## 2021 134.14 139.23
## 2022 autoplot(IVAE_GTM,xlab = "años",ylab = "Indice",main = "IVAE-Guatemala, periodo 2009-2022 (Agosto)")+theme_classic()
## proyeccion a seis meses
modelo_GTM<-auto.arima(y = IVAE_GTM)
summary(modelo_GTM)## Series: IVAE_GTM
## ARIMA(1,0,1)(1,1,1)[12] with drift
##
## Coefficients:
## ar1 ma1 sar1 sma1 drift
## 0.7726 0.2240 0.0450 -0.8045 0.3059
## s.e. 0.0641 0.1021 0.1165 0.1031 0.0154
##
## sigma^2 = 1.994: log likelihood = -266.68
## AIC=545.35 AICc=545.95 BIC=563.38
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.01572868 1.335433 0.8857395 -0.02907498 0.801108 0.2033955
## ACF1
## Training set 0.005568071pronosticos_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 87.7900 85.6900 83.9200 87.0900 85.9000 84.6500 87.0600 87.9400
## 2010 89.6800 88.2800 87.4900 88.0300 87.3500 86.9200 88.6900 91.3500
## 2011 93.6000 92.2000 91.6000 92.6500 92.6100 92.0800 91.7800 95.8600
## 2012 95.1300 95.5800 94.1300 94.9700 95.3100 94.0200 96.3200 98.9200
## 2013 101.2000 99.5000 96.7200 98.6400 98.6700 97.7200 99.4800 102.1600
## 2014 104.8000 104.4000 101.0500 103.7800 102.2000 101.7800 103.9000 107.0900
## 2015 107.6600 106.6700 105.6300 108.7200 107.5300 106.6400 108.4500 111.4400
## 2016 112.2900 111.1200 108.4000 109.3500 110.4100 109.8000 110.4300 114.9900
## 2017 114.7000 113.7200 111.6300 113.8200 113.9300 112.0700 113.6800 116.9100
## 2018 119.5900 118.7100 116.3500 118.2200 118.0400 115.4200 117.9800 121.0400
## 2019 123.9500 123.6700 120.4500 122.9300 121.9400 120.7800 122.9900 126.9400
## 2020 112.7300 111.4900 111.5500 118.5000 120.6000 121.7300 125.2000 128.0500
## 2021 130.0600 130.0100 127.5300 131.2200 130.1300 128.7700 130.6200 135.3400
## 2022 135.8300 135.5300 132.0000 135.0900 136.0000 134.5317 135.8623 138.8687
## Sep Oct Nov Dec
## 2009 95.1900 88.4300 87.0900 94.1400
## 2010 98.9200 92.1600 91.2800 96.9600
## 2011 101.4300 95.0500 94.9500 101.1000
## 2012 104.1100 99.0700 98.8100 101.7200
## 2013 106.3000 102.7500 102.5700 106.7600
## 2014 112.2700 107.7600 107.1500 111.7400
## 2015 115.2400 109.7400 109.4400 112.9600
## 2016 120.6300 115.4200 114.3000 118.0700
## 2017 122.5600 117.7500 117.7700 121.7700
## 2018 125.2000 122.0800 122.7600 126.0500
## 2019 130.4500 127.0100 125.5100 121.3800
## 2020 135.0400 128.8800 128.6100 133.2900
## 2021 140.7700 134.9500 134.1400 139.2300
## 2022 143.7139 138.2934 137.4929Descomposicion 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 87.30065 87.41427 87.52789 87.65473 87.78157 87.92346 88.06535
## 2010 89.10561 89.35189 89.59817 89.86876 90.13935 90.42071 90.70207
## 2011 93.00123 93.32008 93.63894 93.88954 94.14014 94.36208 94.58403
## 2012 96.01437 96.29582 96.57727 96.83453 97.09180 97.35119 97.61059
## 2013 99.44303 99.71463 99.98623 100.24512 100.50401 100.80123 101.09844
## 2014 103.46269 103.87193 104.28118 104.66053 105.03989 105.35275 105.66560
## 2015 107.92923 108.26762 108.60601 108.83048 109.05495 109.24868 109.44240
## 2016 110.81178 111.15119 111.49061 111.88633 112.28206 112.62379 112.96552
## 2017 114.62897 114.81901 115.00905 115.21206 115.41507 115.71371 116.01236
## 2018 118.35470 118.66864 118.98257 119.26685 119.55113 119.86659 120.18204
## 2019 122.73831 123.16575 123.59320 123.84742 124.10165 123.84048 123.57932
## 2020 120.53571 120.67615 120.81658 121.37153 121.92648 123.00460 124.08272
## 2021 129.99082 130.51861 131.04641 131.53356 132.02070 132.48333 132.94596
## 2022 135.51901 135.84268 136.16634 136.40458 136.64281 136.87568 137.10855
## Aug Sep Oct Nov Dec
## 2009 88.21655 88.36775 88.53946 88.71117 88.90839
## 2010 91.03990 91.37774 91.78978 92.20183 92.60153
## 2011 94.79766 95.01130 95.24467 95.47804 95.74620
## 2012 97.91007 98.20955 98.52108 98.83261 99.13782
## 2013 101.46620 101.83396 102.24215 102.65033 103.05651
## 2014 105.98288 106.30015 106.69599 107.09182 107.51053
## 2015 109.66509 109.88779 110.10302 110.31825 110.56502
## 2016 113.25418 113.54283 113.83923 114.13563 114.38230
## 2017 116.39742 116.78248 117.19241 117.60234 117.97852
## 2018 120.56411 120.94618 121.38235 121.81852 122.27842
## 2019 122.87916 122.17900 121.57151 120.96402 120.74987
## 2020 125.37921 126.67571 127.67448 128.67326 129.33204
## 2021 133.37638 133.80679 134.23754 134.66829 135.09365
## 2022 137.33009 137.55163 137.75653 137.96143calculo de la 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
## Dec 2021 135.0937 0.3158596 4.454900 6.816340 3.315395 NA
## Jan 2022 135.5190 0.3148651 4.252762 6.511500 3.131033 NA
## Feb 2022 135.8427 0.2388321 4.079160 6.169699 2.964326 NA
## Mar 2022 136.1663 0.2382630 3.906957 5.791918 2.798692 NA
## Apr 2022 136.4046 0.1749599 3.703252 5.407591 2.621465 NA
## May 2022 136.6428 0.1746543 3.501050 5.016796 2.445373 NA
## Jun 2022 136.8757 0.1704193 3.315395 4.657827 NA NA
## Jul 2022 137.1085 0.1701294 3.131033 4.329675 NA NA
## Aug 2022 137.3301 0.1615828 2.964326 4.049410 NA NA
## Sep 2022 137.5516 0.1613221 2.798692 3.815839 NA NA
## Oct 2022 137.7565 0.1489616 2.621465 3.607321 NA NA
## Nov 2022 137.9614 0.1487400 2.445373 3.423323 NA NAGrafico de las tasas
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()
El salvador
DATA %>% 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 83.92 91.42 93.46 86.39 86.72 87.57 85.27 91.86 99.64 85.56
## 2010 85.94 94.33 92.23 87.18 90.25 89.00 88.74 93.13 100.74 90.27
## 2011 90.79 98.50 97.59 92.16 94.22 92.33 89.06 96.86 103.91 92.65
## 2012 91.23 102.83 102.84 93.61 98.21 93.94 93.49 99.61 105.05 95.67
## 2013 96.34 103.08 101.58 96.42 98.96 97.74 96.22 101.24 108.37 98.70
## 2014 97.12 103.86 104.73 98.48 98.60 98.25 96.43 100.64 107.19 98.87
## 2015 98.75 105.65 105.45 101.67 101.06 100.64 100.44 104.90 109.86 99.25
## 2016 103.43 107.76 110.71 104.01 106.24 104.83 102.04 106.50 114.98 101.41
## 2017 101.40 110.85 113.63 105.51 107.88 106.21 103.28 110.39 117.56 105.17
## 2018 107.93 112.46 113.55 108.80 111.94 107.54 105.81 112.16 120.03 108.10
## 2019 109.95 114.95 114.86 111.24 113.28 111.66 108.32 116.10 122.08 109.49
## 2020 87.36 89.33 96.05 96.95 103.34 106.72 106.12 110.70 119.86 106.84
## 2021 109.72 115.43 115.19 112.16 114.23 113.82 109.73 116.70 123.69 109.25
## 2022 111.15 120.33 118.27 113.36 116.30
## Nov Dec
## 2009 84.69 90.90
## 2010 86.73 94.32
## 2011 91.20 98.46
## 2012 90.77 96.12
## 2013 94.70 101.30
## 2014 94.82 103.15
## 2015 97.76 102.58
## 2016 98.97 108.44
## 2017 102.53 108.39
## 2018 106.41 113.02
## 2019 109.27 104.04
## 2020 107.04 114.51
## 2021 110.28 118.85
## 2022 autoplot(IVAE_ESA,xlab = "años",ylab = "Indice",main = "IVAE-El Salvador total, periodo 2009-2022 (Agosto)")+theme_classic()
## proyeccion a seis meses
modelo_ESA<-auto.arima(y = IVAE_ESA)
summary(modelo_ESA)## Series: IVAE_ESA
## ARIMA(1,0,0)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 sma1 drift
## 0.8093 -0.8396 0.1633
## s.e. 0.0481 0.0862 0.0231
##
## sigma^2 = 5.91: log likelihood = -349.99
## AIC=707.97 AICc=708.25 BIC=719.99
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.06125538 2.314982 1.508332 0.02501818 1.485347 0.3913107
## ACF1
## Training set 0.1204183pronosticos_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$mean))),start = c(2009,1),frequency = 12)
print(IVAE_ESA_Amp)## Jan Feb Mar Apr May Jun Jul Aug
## 2009 83.9200 91.4200 93.4600 86.3900 86.7200 87.5700 85.2700 91.8600
## 2010 85.9400 94.3300 92.2300 87.1800 90.2500 89.0000 88.7400 93.1300
## 2011 90.7900 98.5000 97.5900 92.1600 94.2200 92.3300 89.0600 96.8600
## 2012 91.2300 102.8300 102.8400 93.6100 98.2100 93.9400 93.4900 99.6100
## 2013 96.3400 103.0800 101.5800 96.4200 98.9600 97.7400 96.2200 101.2400
## 2014 97.1200 103.8600 104.7300 98.4800 98.6000 98.2500 96.4300 100.6400
## 2015 98.7500 105.6500 105.4500 101.6700 101.0600 100.6400 100.4400 104.9000
## 2016 103.4300 107.7600 110.7100 104.0100 106.2400 104.8300 102.0400 106.5000
## 2017 101.4000 110.8500 113.6300 105.5100 107.8800 106.2100 103.2800 110.3900
## 2018 107.9300 112.4600 113.5500 108.8000 111.9400 107.5400 105.8100 112.1600
## 2019 109.9500 114.9500 114.8600 111.2400 113.2800 111.6600 108.3200 116.1000
## 2020 87.3600 89.3300 96.0500 96.9500 103.3400 106.7200 106.1200 110.7000
## 2021 109.7200 115.4300 115.1900 112.1600 114.2300 113.8200 109.7300 116.7000
## 2022 111.1500 120.3300 118.2700 113.3600 116.3000 115.3016 112.9833 118.8693
## Sep Oct Nov Dec
## 2009 99.6400 85.5600 84.6900 90.9000
## 2010 100.7400 90.2700 86.7300 94.3200
## 2011 103.9100 92.6500 91.2000 98.4600
## 2012 105.0500 95.6700 90.7700 96.1200
## 2013 108.3700 98.7000 94.7000 101.3000
## 2014 107.1900 98.8700 94.8200 103.1500
## 2015 109.8600 99.2500 97.7600 102.5800
## 2016 114.9800 101.4100 98.9700 108.4400
## 2017 117.5600 105.1700 102.5300 108.3900
## 2018 120.0300 108.1000 106.4100 113.0200
## 2019 122.0800 109.4900 109.2700 104.0400
## 2020 119.8600 106.8400 107.0400 114.5100
## 2021 123.6900 109.2500 110.2800 118.8500
## 2022 126.0136 113.7201 112.3199descomposicion de la serie temporal
FIT_ESA<-stl(IVAE_ESA_Amp,"periodic")
autoplot(FIT_ESA)+theme_classic()
TC_ESA<-FIT_ESA$time.series[,2]
print(TC_ESA)## Jan Feb Mar Apr May Jun Jul
## 2009 88.35172 88.46289 88.57405 88.69332 88.81259 88.93169 89.05079
## 2010 89.86613 90.04124 90.21634 90.43327 90.65020 90.94366 91.23712
## 2011 93.41385 93.63287 93.85189 94.06599 94.28010 94.49675 94.71341
## 2012 96.56929 96.77572 96.98214 97.05352 97.12490 97.12837 97.13183
## 2013 98.00267 98.25016 98.49764 98.75765 99.01766 99.24125 99.46483
## 2014 100.21705 100.17725 100.13745 100.08723 100.03700 100.06763 100.09825
## 2015 101.49381 101.75884 102.02386 102.16473 102.30560 102.41098 102.51636
## 2016 104.38272 104.68391 104.98511 105.19916 105.41321 105.51284 105.61247
## 2017 106.62634 106.88566 107.14497 107.38027 107.61558 107.79611 107.97665
## 2018 109.28069 109.50249 109.72430 109.94113 110.15797 110.38238 110.60679
## 2019 111.95656 112.19561 112.43465 112.48421 112.53377 111.85991 111.18604
## 2020 103.25160 102.84560 102.43959 102.68874 102.93788 104.13660 105.33532
## 2021 112.31467 112.71732 113.11997 113.38519 113.65041 113.88689 114.12338
## 2022 115.46592 115.66211 115.85830 116.01295 116.16760 116.31033 116.45305
## Aug Sep Oct Nov Dec
## 2009 89.16294 89.27509 89.40572 89.53635 89.70124
## 2010 91.62665 92.01618 92.42296 92.82974 93.12179
## 2011 94.99548 95.27755 95.61920 95.96084 96.26507
## 2012 97.18697 97.24210 97.39526 97.54842 97.77555
## 2013 99.63782 99.81082 99.95770 100.10459 100.16082
## 2014 100.24237 100.38649 100.63063 100.87477 101.18429
## 2015 102.74528 102.97420 103.32132 103.66843 104.02557
## 2016 105.71390 105.81532 105.99691 106.17849 106.40242
## 2017 108.14376 108.31087 108.54409 108.77730 109.02900
## 2018 110.79414 110.98148 111.20739 111.43329 111.69493
## 2019 109.68386 108.18168 106.66399 105.14631 104.19895
## 2020 106.98995 108.64458 109.82823 111.01187 111.66327
## 2021 114.37382 114.62427 114.84248 115.06069 115.26331
## 2022 116.58456 116.71606 116.83892 116.96178calculo de la 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
## Dec 2021 115.2633 0.1760928 3.224014 7.516211 2.127934 NA
## Jan 2022 115.4659 0.1757832 2.805739 7.001468 2.041368 NA
## Feb 2022 115.6621 0.1699128 2.612549 6.411243 1.932902 NA
## Mar 2022 115.8583 0.1696246 2.420735 5.748352 1.824910 NA
## Apr 2022 116.0130 0.1334808 2.317556 5.086538 1.738414 NA
## May 2022 116.1676 0.1333029 2.214859 4.425800 1.652245 NA
## Jun 2022 116.3103 0.1228620 2.127934 3.843579 NA NA
## Jul 2022 116.4531 0.1227112 2.041368 3.336908 NA NA
## Aug 2022 116.5846 0.1129246 1.932902 2.935081 NA NA
## Sep 2022 116.7161 0.1127973 1.824910 2.635132 NA NA
## Oct 2022 116.8389 0.1052628 1.738414 2.403469 NA NA
## Nov 2022 116.9618 0.1051521 1.652245 2.238847 NA NAGrafico 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()
Honduras
DATA %>% 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 156.18 164.17 163.04 155.42 159.89 157.82 166.33 163.97 176.16 165.28
## 2010 165.46 173.89 171.00 162.53 166.65 175.18 172.00 175.48 186.89 176.96
## 2011 175.18 184.30 182.33 175.83 185.67 182.03 185.82 188.18 198.66 181.51
## 2012 183.81 193.45 192.28 185.89 193.61 188.79 199.97 199.48 203.10 189.68
## 2013 195.49 199.00 194.38 190.45 196.66 191.32 201.79 201.54 213.57 194.20
## 2014 197.36 207.03 198.09 194.18 199.21 197.73 205.50 203.26 221.72 200.82
## 2015 206.39 206.66 206.13 201.94 207.78 204.91 213.81 214.73 231.40 207.87
## 2016 211.07 214.45 216.00 205.61 215.98 212.31 220.76 227.59 245.58 219.37
## 2017 218.03 225.53 225.90 216.75 229.08 226.26 232.75 235.80 251.23 228.97
## 2018 227.12 234.88 234.03 225.04 238.66 232.55 244.93 245.16 262.48 235.30
## 2019 234.80 241.51 235.46 238.02 244.65 239.69 252.72 250.26 273.80 242.49
## 2020 186.88 189.07 208.71 209.30 225.80 230.24 249.34 218.89 258.08 229.97
## 2021 235.96 242.36 247.40 239.81 256.77 246.87 265.45 264.73 279.05 247.40
## 2022 248.85 254.82 256.38 246.21 272.05
## Nov Dec
## 2009 166.91 179.91
## 2010 179.46 190.71
## 2011 189.25 202.52
## 2012 192.66 196.37
## 2013 197.58 205.41
## 2014 202.02 214.06
## 2015 210.56 220.51
## 2016 221.50 233.93
## 2017 228.12 237.11
## 2018 235.08 246.40
## 2019 241.65 218.27
## 2020 236.28 251.05
## 2021 246.66 263.79
## 2022autoplot(IVAE_HND,xlab = "años",ylab = "Indice",main = "IVAE-Honduras, periodo 2009-2022 (Agosto)")+theme_classic()
## proyeccion 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
## 0.7871 -0.6818 0.5898
## s.e. 0.0511 0.0673 0.0775
##
## sigma^2 = 41.35: log likelihood = -491.4
## AIC=990.79 AICc=991.07 BIC=1002.81
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.1047523 6.123734 3.690745 -0.002721073 1.707085 0.3251162
## ACF1
## Training set -0.03132499pronosticos_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 156.1800 164.1700 163.0400 155.4200 159.8900 157.8200 166.3300 163.9700
## 2010 165.4600 173.8900 171.0000 162.5300 166.6500 175.1800 172.0000 175.4800
## 2011 175.1800 184.3000 182.3300 175.8300 185.6700 182.0300 185.8200 188.1800
## 2012 183.8100 193.4500 192.2800 185.8900 193.6100 188.7900 199.9700 199.4800
## 2013 195.4900 199.0000 194.3800 190.4500 196.6600 191.3200 201.7900 201.5400
## 2014 197.3600 207.0300 198.0900 194.1800 199.2100 197.7300 205.5000 203.2600
## 2015 206.3900 206.6600 206.1300 201.9400 207.7800 204.9100 213.8100 214.7300
## 2016 211.0700 214.4500 216.0000 205.6100 215.9800 212.3100 220.7600 227.5900
## 2017 218.0300 225.5300 225.9000 216.7500 229.0800 226.2600 232.7500 235.8000
## 2018 227.1200 234.8800 234.0300 225.0400 238.6600 232.5500 244.9300 245.1600
## 2019 234.8000 241.5100 235.4600 238.0200 244.6500 239.6900 252.7200 250.2600
## 2020 186.8800 189.0700 208.7100 209.3000 225.8000 230.2400 249.3400 218.8900
## 2021 235.9600 242.3600 247.4000 239.8100 256.7700 246.8700 265.4500 264.7300
## 2022 248.8500 254.8200 256.3800 246.2100 272.0500 265.0107 277.6120 269.1743
## Sep Oct Nov Dec
## 2009 176.1600 165.2800 166.9100 179.9100
## 2010 186.8900 176.9600 179.4600 190.7100
## 2011 198.6600 181.5100 189.2500 202.5200
## 2012 203.1000 189.6800 192.6600 196.3700
## 2013 213.5700 194.2000 197.5800 205.4100
## 2014 221.7200 200.8200 202.0200 214.0600
## 2015 231.4000 207.8700 210.5600 220.5100
## 2016 245.5800 219.3700 221.5000 233.9300
## 2017 251.2300 228.9700 228.1200 237.1100
## 2018 262.4800 235.3000 235.0800 246.4000
## 2019 273.8000 242.4900 241.6500 218.2700
## 2020 258.0800 229.9700 236.2800 251.0500
## 2021 279.0500 247.4000 246.6600 263.7900
## 2022 289.3537 260.0122 260.4492descomposicion 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 162.2026 162.5905 162.9784 163.4914 164.0044 164.5974 165.1904 165.7745
## 2010 170.0625 170.8712 171.6798 172.3657 173.0515 173.8096 174.5676 175.6029
## 2011 181.6823 182.5872 183.4921 184.0751 184.6581 185.2603 185.8626 186.6816
## 2012 191.3548 192.1322 192.9097 193.2262 193.5428 193.6493 193.7559 194.0693
## 2013 196.0300 196.4392 196.8485 197.2220 197.5956 197.9030 198.2105 198.6273
## 2014 201.0363 201.4165 201.7968 202.1339 202.4710 202.8612 203.2515 203.7980
## 2015 207.4799 208.2086 208.9373 209.5022 210.0670 210.5673 211.0675 211.6756
## 2016 215.1077 215.9316 216.7555 217.6710 218.5864 219.4996 220.4127 221.3605
## 2017 226.3394 227.0729 227.8065 228.3845 228.9626 229.5291 230.0956 230.7521
## 2018 234.4188 235.2326 236.0464 236.7865 237.5266 238.1578 238.7890 239.3205
## 2019 242.1545 242.8148 243.4752 243.8484 244.2217 242.8319 241.4421 238.3446
## 2020 225.1198 223.7533 222.3868 222.5343 222.6819 225.1232 227.5645 230.9409
## 2021 243.9566 246.1704 248.3842 250.0741 251.7640 252.8469 253.9297 254.7207
## 2022 258.9324 260.0675 261.2025 262.1618 263.1211 264.1111 265.1012 266.1155
## Sep Oct Nov Dec
## 2009 166.3587 167.1918 168.0249 169.0437
## 2010 176.6382 177.9134 179.1886 180.4355
## 2011 187.5006 188.4352 189.3698 190.3623
## 2012 194.3827 194.7974 195.2120 195.6210
## 2013 199.0442 199.5699 200.0957 200.5660
## 2014 204.3446 205.1133 205.8820 206.6810
## 2015 212.2836 212.9973 213.7109 214.4093
## 2016 222.3082 223.3642 224.4203 225.3798
## 2017 231.4086 232.1528 232.8969 233.6579
## 2018 239.8520 240.4328 241.0137 241.5841
## 2019 235.2470 232.2426 229.2383 227.1790
## 2020 234.3173 236.9427 239.5681 241.7623
## 2021 255.5117 256.2806 257.0495 257.9910
## 2022 267.1298 268.0991 269.0685calculo de la 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
## Dec 2021 257.9910 0.3662424 6.712642 10.025965 4.454978 NA
## Jan 2022 258.9324 0.3649059 6.138710 9.818513 4.399415 NA
## Feb 2022 260.0675 0.4383657 5.645310 9.434790 4.473415 NA
## Mar 2022 261.2025 0.4364525 5.160705 8.880460 4.546957 NA
## Apr 2022 262.1618 0.3672529 4.833667 8.251918 4.611541 NA
## May 2022 263.1211 0.3659091 4.511019 7.552308 4.675738 NA
## Jun 2022 264.1111 0.3762691 4.454978 6.913489 NA NA
## Jul 2022 265.1012 0.3748586 4.399415 6.332850 NA NA
## Aug 2022 266.1155 0.3826064 4.473415 5.862675 NA NA
## Sep 2022 267.1298 0.3811481 4.546957 5.499089 NA NA
## Oct 2022 268.0991 0.3628857 4.611541 5.212512 NA NA
## Nov 2022 269.0685 0.3615736 4.675738 5.000932 NA NAGrafico 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
DATA %>% 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 156.18 164.17 163.04 155.42 159.89 157.82 166.33 163.97 176.16 165.28
## 2010 165.46 173.89 171.00 162.53 166.65 175.18 172.00 175.48 186.89 176.96
## 2011 175.18 184.30 182.33 175.83 185.67 182.03 185.82 188.18 198.66 181.51
## 2012 183.81 193.45 192.28 185.89 193.61 188.79 199.97 199.48 203.10 189.68
## 2013 195.49 199.00 194.38 190.45 196.66 191.32 201.79 201.54 213.57 194.20
## 2014 197.36 207.03 198.09 194.18 199.21 197.73 205.50 203.26 221.72 200.82
## 2015 206.39 206.66 206.13 201.94 207.78 204.91 213.81 214.73 231.40 207.87
## 2016 211.07 214.45 216.00 205.61 215.98 212.31 220.76 227.59 245.58 219.37
## 2017 218.03 225.53 225.90 216.75 229.08 226.26 232.75 235.80 251.23 228.97
## 2018 227.12 234.88 234.03 225.04 238.66 232.55 244.93 245.16 262.48 235.30
## 2019 234.80 241.51 235.46 238.02 244.65 239.69 252.72 250.26 273.80 242.49
## 2020 186.88 189.07 208.71 209.30 225.80 230.24 249.34 218.89 258.08 229.97
## 2021 235.96 242.36 247.40 239.81 256.77 246.87 265.45 264.73 279.05 247.40
## 2022 248.85 254.82 256.38 246.21 272.05
## Nov Dec
## 2009 166.91 179.91
## 2010 179.46 190.71
## 2011 189.25 202.52
## 2012 192.66 196.37
## 2013 197.58 205.41
## 2014 202.02 214.06
## 2015 210.56 220.51
## 2016 221.50 233.93
## 2017 228.12 237.11
## 2018 235.08 246.40
## 2019 241.65 218.27
## 2020 236.28 251.05
## 2021 246.66 263.79
## 2022 autoplot(IVAE_NIC,xlab = "años",ylab = "Indice",main = "IVAE-Nicaragua, periodo 2009-2022 (Agosto)")+theme_classic()
## proyeccion 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
## 0.7871 -0.6818 0.5898
## s.e. 0.0511 0.0673 0.0775
##
## sigma^2 = 41.35: log likelihood = -491.4
## AIC=990.79 AICc=991.07 BIC=1002.81
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.1047523 6.123734 3.690745 -0.002721073 1.707085 0.3251162
## ACF1
## Training set -0.03132499pronosticos_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$mean))),start = c(2009,1),frequency = 12)
print(IVAE_NIC_Amp)## Jan Feb Mar Apr May Jun Jul Aug
## 2009 156.1800 164.1700 163.0400 155.4200 159.8900 157.8200 166.3300 163.9700
## 2010 165.4600 173.8900 171.0000 162.5300 166.6500 175.1800 172.0000 175.4800
## 2011 175.1800 184.3000 182.3300 175.8300 185.6700 182.0300 185.8200 188.1800
## 2012 183.8100 193.4500 192.2800 185.8900 193.6100 188.7900 199.9700 199.4800
## 2013 195.4900 199.0000 194.3800 190.4500 196.6600 191.3200 201.7900 201.5400
## 2014 197.3600 207.0300 198.0900 194.1800 199.2100 197.7300 205.5000 203.2600
## 2015 206.3900 206.6600 206.1300 201.9400 207.7800 204.9100 213.8100 214.7300
## 2016 211.0700 214.4500 216.0000 205.6100 215.9800 212.3100 220.7600 227.5900
## 2017 218.0300 225.5300 225.9000 216.7500 229.0800 226.2600 232.7500 235.8000
## 2018 227.1200 234.8800 234.0300 225.0400 238.6600 232.5500 244.9300 245.1600
## 2019 234.8000 241.5100 235.4600 238.0200 244.6500 239.6900 252.7200 250.2600
## 2020 186.8800 189.0700 208.7100 209.3000 225.8000 230.2400 249.3400 218.8900
## 2021 235.9600 242.3600 247.4000 239.8100 256.7700 246.8700 265.4500 264.7300
## 2022 248.8500 254.8200 256.3800 246.2100 272.0500 265.0107 277.6120 269.1743
## Sep Oct Nov Dec
## 2009 176.1600 165.2800 166.9100 179.9100
## 2010 186.8900 176.9600 179.4600 190.7100
## 2011 198.6600 181.5100 189.2500 202.5200
## 2012 203.1000 189.6800 192.6600 196.3700
## 2013 213.5700 194.2000 197.5800 205.4100
## 2014 221.7200 200.8200 202.0200 214.0600
## 2015 231.4000 207.8700 210.5600 220.5100
## 2016 245.5800 219.3700 221.5000 233.9300
## 2017 251.2300 228.9700 228.1200 237.1100
## 2018 262.4800 235.3000 235.0800 246.4000
## 2019 273.8000 242.4900 241.6500 218.2700
## 2020 258.0800 229.9700 236.2800 251.0500
## 2021 279.0500 247.4000 246.6600 263.7900
## 2022 289.3537 260.0122 260.4492descomposicion de la serie temporal
FIT_NIC<-stl(IVAE_NIC_Amp,"periodic")
autoplot(FIT_NIC)+theme_classic()
TC_NIC<-FIT_NIC$time.series[,2]
print(TC_NIC)## Jan Feb Mar Apr May Jun Jul Aug
## 2009 162.2026 162.5905 162.9784 163.4914 164.0044 164.5974 165.1904 165.7745
## 2010 170.0625 170.8712 171.6798 172.3657 173.0515 173.8096 174.5676 175.6029
## 2011 181.6823 182.5872 183.4921 184.0751 184.6581 185.2603 185.8626 186.6816
## 2012 191.3548 192.1322 192.9097 193.2262 193.5428 193.6493 193.7559 194.0693
## 2013 196.0300 196.4392 196.8485 197.2220 197.5956 197.9030 198.2105 198.6273
## 2014 201.0363 201.4165 201.7968 202.1339 202.4710 202.8612 203.2515 203.7980
## 2015 207.4799 208.2086 208.9373 209.5022 210.0670 210.5673 211.0675 211.6756
## 2016 215.1077 215.9316 216.7555 217.6710 218.5864 219.4996 220.4127 221.3605
## 2017 226.3394 227.0729 227.8065 228.3845 228.9626 229.5291 230.0956 230.7521
## 2018 234.4188 235.2326 236.0464 236.7865 237.5266 238.1578 238.7890 239.3205
## 2019 242.1545 242.8148 243.4752 243.8484 244.2217 242.8319 241.4421 238.3446
## 2020 225.1198 223.7533 222.3868 222.5343 222.6819 225.1232 227.5645 230.9409
## 2021 243.9566 246.1704 248.3842 250.0741 251.7640 252.8469 253.9297 254.7207
## 2022 258.9324 260.0675 261.2025 262.1618 263.1211 264.1111 265.1012 266.1155
## Sep Oct Nov Dec
## 2009 166.3587 167.1918 168.0249 169.0437
## 2010 176.6382 177.9134 179.1886 180.4355
## 2011 187.5006 188.4352 189.3698 190.3623
## 2012 194.3827 194.7974 195.2120 195.6210
## 2013 199.0442 199.5699 200.0957 200.5660
## 2014 204.3446 205.1133 205.8820 206.6810
## 2015 212.2836 212.9973 213.7109 214.4093
## 2016 222.3082 223.3642 224.4203 225.3798
## 2017 231.4086 232.1528 232.8969 233.6579
## 2018 239.8520 240.4328 241.0137 241.5841
## 2019 235.2470 232.2426 229.2383 227.1790
## 2020 234.3173 236.9427 239.5681 241.7623
## 2021 255.5117 256.2806 257.0495 257.9910
## 2022 267.1298 268.0991 269.0685calculo de la 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
## Dec 2021 257.9910 0.3662424 6.712642 10.025965 4.454978 NA
## Jan 2022 258.9324 0.3649059 6.138710 9.818513 4.399415 NA
## Feb 2022 260.0675 0.4383657 5.645310 9.434790 4.473415 NA
## Mar 2022 261.2025 0.4364525 5.160705 8.880460 4.546957 NA
## Apr 2022 262.1618 0.3672529 4.833667 8.251918 4.611541 NA
## May 2022 263.1211 0.3659091 4.511019 7.552308 4.675738 NA
## Jun 2022 264.1111 0.3762691 4.454978 6.913489 NA NA
## Jul 2022 265.1012 0.3748586 4.399415 6.332850 NA NA
## Aug 2022 266.1155 0.3826064 4.473415 5.862675 NA NA
## Sep 2022 267.1298 0.3811481 4.546957 5.499089 NA NA
## Oct 2022 268.0991 0.3628857 4.611541 5.212512 NA NA
## Nov 2022 269.0685 0.3615736 4.675738 5.000932 NA NAGrafico 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()
### Costa Rica
DATA %>% 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 70.13 72.30 73.43 72.93 72.00 73.64 76.77 78.18 78.35 75.10
## 2010 73.27 75.74 76.43 76.13 75.58 77.14 79.74 82.16 81.06 78.27
## 2011 76.03 79.23 79.63 77.99 77.89 80.05 83.57 85.93 84.67 82.37
## 2012 78.55 82.23 81.83 80.60 81.77 82.75 85.69 89.26 88.63 83.10
## 2013 81.13 84.12 83.77 83.88 83.97 86.04 88.53 90.77 90.80 86.41
## 2014 83.12 86.04 85.36 86.63 86.17 88.14 92.55 94.00 95.23 88.30
## 2015 88.50 92.09 92.53 93.84 92.75 93.78 96.67 98.43 97.87 94.53
## 2016 93.13 95.39 95.66 94.94 94.84 98.12 101.26 103.90 103.79 96.71
## 2017 94.84 99.06 99.90 96.26 96.64 98.99 103.96 107.71 108.11 99.21
## 2018 99.62 104.59 103.43 101.46 101.10 101.62 106.09 108.90 108.01 101.48
## 2019 99.98 103.78 103.63 102.45 101.43 103.57 109.05 111.47 111.09 102.20
## 2020 89.65 91.81 95.78 91.86 92.51 97.39 101.72 105.12 110.61 96.63
## 2021 101.66 104.50 104.73 107.77 105.71 108.62 111.23 116.91 119.84 106.31
## 2022 105.61 108.90 109.10 110.06 110.34
## Nov Dec
## 2009 73.53 79.92
## 2010 76.77 82.00
## 2011 82.95 86.03
## 2012 82.79 85.62
## 2013 87.04 89.12
## 2014 90.04 92.86
## 2015 95.60 96.36
## 2016 96.96 100.85
## 2017 99.00 103.55
## 2018 101.93 105.94
## 2019 104.23 102.60
## 2020 100.29 108.09
## 2021 108.14 117.49
## 2022autoplot(IVAE_CRC,xlab = "años",ylab = "Indice",main = "IVAE-Costa Rica, periodo 2009-2022 (Agosto)")+theme_classic()
## proyeccion 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
## 0.8567 -0.5084 0.2385
## s.e. 0.0422 0.0831 0.0449
##
## sigma^2 = 3.416: log likelihood = -303.79
## AIC=615.58 AICc=615.86 BIC=627.6
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.009305651 1.760047 1.16234 -0.01328966 1.212302 0.2862953
## ACF1
## Training set -0.06485786pronosticos_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 70.1300 72.3000 73.4300 72.9300 72.0000 73.6400 76.7700 78.1800
## 2010 73.2700 75.7400 76.4300 76.1300 75.5800 77.1400 79.7400 82.1600
## 2011 76.0300 79.2300 79.6300 77.9900 77.8900 80.0500 83.5700 85.9300
## 2012 78.5500 82.2300 81.8300 80.6000 81.7700 82.7500 85.6900 89.2600
## 2013 81.1300 84.1200 83.7700 83.8800 83.9700 86.0400 88.5300 90.7700
## 2014 83.1200 86.0400 85.3600 86.6300 86.1700 88.1400 92.5500 94.0000
## 2015 88.5000 92.0900 92.5300 93.8400 92.7500 93.7800 96.6700 98.4300
## 2016 93.1300 95.3900 95.6600 94.9400 94.8400 98.1200 101.2600 103.9000
## 2017 94.8400 99.0600 99.9000 96.2600 96.6400 98.9900 103.9600 107.7100
## 2018 99.6200 104.5900 103.4300 101.4600 101.1000 101.6200 106.0900 108.9000
## 2019 99.9800 103.7800 103.6300 102.4500 101.4300 103.5700 109.0500 111.4700
## 2020 89.6500 91.8100 95.7800 91.8600 92.5100 97.3900 101.7200 105.1200
## 2021 101.6600 104.5000 104.7300 107.7700 105.7100 108.6200 111.2300 116.9100
## 2022 105.6100 108.9000 109.1000 110.0600 110.3400 112.9177 116.0813 120.0196
## Sep Oct Nov Dec
## 2009 78.3500 75.1000 73.5300 79.9200
## 2010 81.0600 78.2700 76.7700 82.0000
## 2011 84.6700 82.3700 82.9500 86.0300
## 2012 88.6300 83.1000 82.7900 85.6200
## 2013 90.8000 86.4100 87.0400 89.1200
## 2014 95.2300 88.3000 90.0400 92.8600
## 2015 97.8700 94.5300 95.6000 96.3600
## 2016 103.7900 96.7100 96.9600 100.8500
## 2017 108.1100 99.2100 99.0000 103.5500
## 2018 108.0100 101.4800 101.9300 105.9400
## 2019 111.0900 102.2000 104.2300 102.6000
## 2020 110.6100 96.6300 100.2900 108.0900
## 2021 119.8400 106.3100 108.1400 117.4900
## 2022 122.4002 109.9274 111.7963descomposcion 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 73.81242 73.96758 74.12274 74.30621 74.48969 74.69909 74.90849
## 2010 76.57376 76.86001 77.14626 77.34891 77.55155 77.73655 77.92155
## 2011 79.49236 79.80017 80.10798 80.42288 80.73777 81.04196 81.34614
## 2012 82.96342 83.16400 83.36459 83.46431 83.56402 83.63533 83.70664
## 2013 84.91359 85.14165 85.36972 85.61276 85.85580 86.09431 86.33282
## 2014 87.53990 87.81109 88.08228 88.35010 88.61793 88.95768 89.29744
## 2015 92.38207 92.79784 93.21360 93.57726 93.94093 94.25289 94.56486
## 2016 96.21767 96.61109 97.00451 97.30490 97.60530 97.83301 98.06073
## 2017 99.26762 99.51902 99.77042 100.03779 100.30516 100.57330 100.84144
## 2018 102.87803 103.03814 103.19824 103.32132 103.44440 103.50452 103.56464
## 2019 104.08570 104.29415 104.50261 104.62907 104.75554 104.48647 104.21741
## 2020 99.43598 98.89547 98.35495 98.23351 98.11207 98.58681 99.06154
## 2021 105.02675 105.91918 106.81162 107.58532 108.35903 108.95969 109.56034
## 2022 111.50057 111.82616 112.15174 112.41524 112.67873 112.94159 113.20444
## Aug Sep Oct Nov Dec
## 2009 75.12355 75.33862 75.61162 75.88461 76.22919
## 2010 78.14688 78.37221 78.63119 78.89017 79.19127
## 2011 81.62213 81.89812 82.16774 82.43736 82.70039
## 2012 83.85409 84.00155 84.21599 84.43043 84.67201
## 2013 86.51505 86.69727 86.87960 87.06192 87.30091
## 2014 89.77022 90.24300 90.79503 91.34705 91.86456
## 2015 94.80998 95.05509 95.29274 95.53038 95.87402
## 2016 98.29585 98.53098 98.72390 98.91683 99.09222
## 2017 101.19633 101.55123 101.93736 102.32348 102.60076
## 2018 103.57964 103.59464 103.67396 103.75328 103.91949
## 2019 103.51160 102.80580 101.92115 101.03650 100.23624
## 2020 100.00776 100.95398 102.01255 103.07112 104.04893
## 2021 109.94769 110.33504 110.63315 110.93126 111.21591
## 2022 113.48249 113.76054 114.04284 114.32514calculo de la 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
## Dec 2021 111.2159 0.2566046 6.888088 8.703574 3.654471 NA
## Jan 2022 111.5006 0.2559478 6.163973 8.736438 3.326107 NA
## Feb 2022 111.8262 0.2920047 5.576868 8.593832 3.214981 NA
## Mar 2022 112.1517 0.2911545 4.999574 8.279284 3.104635 NA
## Apr 2022 112.4152 0.2349448 4.489382 7.849120 3.081979 NA
## May 2022 112.6787 0.2343941 3.986475 7.306885 3.059446 NA
## Jun 2022 112.9416 0.2332764 3.654471 6.735802 NA NA
## Jul 2022 113.2044 0.2327335 3.326107 6.136754 NA NA
## Aug 2022 113.4825 0.2456169 3.214981 5.585362 NA NA
## Sep 2022 113.7605 0.2450151 3.104635 5.079796 NA NA
## Oct 2022 114.0428 0.2481551 3.081979 4.641990 NA NA
## Nov 2022 114.3251 0.2475408 3.059446 4.269826 NA NAGrafico 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()
### Panamá
DATA%>% 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 188.06 193.65 199.60 188.20 190.96 195.45 204.89 185.82 190.56 201.01
## 2010 203.11 202.73 210.75 198.31 206.22 205.20 213.91 202.49 205.63 212.36
## 2011 218.93 227.12 226.92 210.41 225.69 222.92 233.74 226.59 231.09 233.23
## 2012 237.89 248.64 251.02 239.86 246.47 238.38 249.62 251.28 247.71 253.29
## 2013 262.60 268.38 269.16 256.27 265.08 259.72 280.51 272.24 270.52 265.09
## 2014 275.53 274.45 283.35 268.30 278.43 272.53 296.66 282.62 292.03 281.48
## 2015 280.85 281.06 294.76 279.85 290.40 283.40 310.57 295.47 300.89 292.53
## 2016 292.13 296.56 306.77 293.76 303.34 296.96 322.82 309.18 312.22 305.84
## 2017 309.60 316.06 324.68 304.97 318.19 310.13 335.94 322.13 324.80 320.57
## 2018 311.69 317.94 324.94 308.98 323.54 315.15 333.20 328.79 330.41 332.39
## 2019 319.17 325.72 332.45 325.11 336.06 332.01 346.53 341.09 341.27 346.02
## 2020 243.21 222.17 233.12 240.66 242.56 259.86 298.72 296.58 339.78 304.69
## 2021 307.26 314.85 310.08 306.64 319.01 318.06 344.23 332.56 396.05 354.21
## 2022 334.97 344.42 348.03 317.49 360.23
## Nov Dec
## 2009 200.42 220.03
## 2010 218.50 228.61
## 2011 237.88 260.05
## 2012 254.04 276.60
## 2013 267.31 286.56
## 2014 276.75 307.31
## 2015 289.67 318.79
## 2016 307.27 344.01
## 2017 323.91 349.99
## 2018 332.73 353.65
## 2019 341.78 357.06
## 2020 322.92 355.12
## 2021 368.62 390.51
## 2022 autoplot(IVAE_PAN,xlab = "años",ylab = "Indice",main = "IVAE-Panamá, periodo 2009-2022 (Agosto)")+theme_classic()
## proyeccion 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
## 0.8605 -0.0077 0.1292 -0.4583 1.0458
## s.e. 0.0540 0.1019 0.0885 0.1012 0.3550
##
## sigma^2 = 138.1: log likelihood = -578.13
## AIC=1168.25 AICc=1168.84 BIC=1186.27
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.02581271 11.11392 6.021466 -0.08718314 2.119036 0.2781174
## ACF1
## Training set 0.002010758pronosticos_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$mean))),start = c(2009,1),frequency = 12)
print(IVAE_PAN_Amp)## Jan Feb Mar Apr May Jun Jul Aug
## 2009 188.0600 193.6500 199.6000 188.2000 190.9600 195.4500 204.8900 185.8200
## 2010 203.1100 202.7300 210.7500 198.3100 206.2200 205.2000 213.9100 202.4900
## 2011 218.9300 227.1200 226.9200 210.4100 225.6900 222.9200 233.7400 226.5900
## 2012 237.8900 248.6400 251.0200 239.8600 246.4700 238.3800 249.6200 251.2800
## 2013 262.6000 268.3800 269.1600 256.2700 265.0800 259.7200 280.5100 272.2400
## 2014 275.5300 274.4500 283.3500 268.3000 278.4300 272.5300 296.6600 282.6200
## 2015 280.8500 281.0600 294.7600 279.8500 290.4000 283.4000 310.5700 295.4700
## 2016 292.1300 296.5600 306.7700 293.7600 303.3400 296.9600 322.8200 309.1800
## 2017 309.6000 316.0600 324.6800 304.9700 318.1900 310.1300 335.9400 322.1300
## 2018 311.6900 317.9400 324.9400 308.9800 323.5400 315.1500 333.2000 328.7900
## 2019 319.1700 325.7200 332.4500 325.1100 336.0600 332.0100 346.5300 341.0900
## 2020 243.2100 222.1700 233.1200 240.6600 242.5600 259.8600 298.7200 296.5800
## 2021 307.2600 314.8500 310.0800 306.6400 319.0100 318.0600 344.2300 332.5600
## 2022 334.9700 344.4200 348.0300 317.4900 360.2300 354.3208 382.3039 370.1817
## Sep Oct Nov Dec
## 2009 190.5600 201.0100 200.4200 220.0300
## 2010 205.6300 212.3600 218.5000 228.6100
## 2011 231.0900 233.2300 237.8800 260.0500
## 2012 247.7100 253.2900 254.0400 276.6000
## 2013 270.5200 265.0900 267.3100 286.5600
## 2014 292.0300 281.4800 276.7500 307.3100
## 2015 300.8900 292.5300 289.6700 318.7900
## 2016 312.2200 305.8400 307.2700 344.0100
## 2017 324.8000 320.5700 323.9100 349.9900
## 2018 330.4100 332.3900 332.7300 353.6500
## 2019 341.2700 346.0200 341.7800 357.0600
## 2020 339.7800 304.6900 322.9200 355.1200
## 2021 396.0500 354.2100 368.6200 390.5100
## 2022 412.2607 378.4603 387.9674descomposicion de la serie temporal
FIT_PAN<-stl(IVAE_PAN_Amp,"periodic")
autoplot(FIT_PAN)+theme_classic()
TC_PAN<-FIT_PAN$time.series[,2]
print(TC_PAN)## Jan Feb Mar Apr May Jun Jul Aug
## 2009 199.0532 198.7594 198.4657 198.3638 198.2620 198.3782 198.4944 198.7071
## 2010 203.3032 204.6861 206.0689 207.0308 207.9928 208.7486 209.5043 210.6268
## 2011 219.1133 221.2096 223.3059 225.0437 226.7816 228.2644 229.7472 231.4714
## 2012 241.7725 243.5827 245.3929 246.6975 248.0022 249.0657 250.1292 251.4857
## 2013 261.2041 263.3253 265.4466 266.6942 267.9419 268.4590 268.9761 269.5420
## 2014 275.1797 276.7929 278.4062 279.7390 281.0719 281.7936 282.5153 283.0262
## 2015 287.6674 288.9583 290.2493 291.3167 292.3841 293.1317 293.8793 294.6275
## 2016 300.1020 301.5141 302.9263 304.2720 305.6178 306.8663 308.1148 309.3744
## 2017 316.1239 317.4758 318.8278 319.8469 320.8659 321.2529 321.6399 321.7426
## 2018 323.2227 323.7357 324.2487 324.8459 325.4430 325.9269 326.4108 327.0018
## 2019 332.1962 333.4286 334.6611 335.5180 336.3749 334.7256 333.0762 327.1136
## 2020 290.0575 285.4719 280.8862 279.6296 278.3729 281.4546 284.5364 290.9055
## 2021 317.9187 321.3949 324.8711 328.5569 332.2428 335.9161 339.5894 342.6681
## 2022 355.1850 357.6104 360.0358 362.7019 365.3681 368.0666 370.7651 373.7171
## Sep Oct Nov Dec
## 2009 198.9199 199.6175 200.3151 201.8092
## 2010 211.7493 213.3812 215.0130 217.0632
## 2011 233.1957 235.3538 237.5119 239.6422
## 2012 252.8422 254.7281 256.6140 258.9090
## 2013 270.1080 271.1974 272.2869 273.7333
## 2014 283.5370 284.4072 285.2775 286.4724
## 2015 295.3757 296.4216 297.4676 298.7848
## 2016 310.6340 311.9644 313.2948 314.7093
## 2017 321.8453 322.1057 322.3661 322.7944
## 2018 327.5927 328.5862 329.5797 330.8879
## 2019 321.1511 312.8945 304.6380 297.3477
## 2020 297.2746 303.3060 309.3374 313.6281
## 2021 345.7468 348.2761 350.8054 352.9952
## 2022 376.6691 379.9004 383.1316calculo de la 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
## Dec 2021 352.9952 0.6242294 12.552161 15.62639 9.571008 NA
## Jan 2022 355.1850 0.6203569 11.721952 15.76978 9.180437 NA
## Feb 2022 357.6104 0.6828491 11.268225 15.61881 9.060978 NA
## Mar 2022 360.0358 0.6782179 10.824208 15.18329 8.943646 NA
## Apr 2022 362.7019 0.7405248 10.392421 14.57503 9.080240 NA
## May 2022 365.3681 0.7350813 9.970215 13.80329 9.214863 NA
## Jun 2022 368.0666 0.7385762 9.571008 13.00996 NA NA
## Jul 2022 370.7651 0.7331613 9.180437 12.19633 NA NA
## Aug 2022 373.7171 0.7961917 9.060978 11.49737 NA NA
## Sep 2022 376.6691 0.7899026 8.943646 10.90678 NA NA
## Oct 2022 379.9004 0.8578437 9.080240 10.44593 NA NA
## Nov 2022 383.1316 0.8505473 9.214863 10.10923 NA NAGrafico 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()