Analisis de Coyuntura

Carga de datos

library(readxl)
IMAE <- read_excel("~/MAE1182022/Coyuntura/IMAEa.xlsx")

library(ggplot2)
library(forecast)

IMAE [,-1] %>% ts(start = c(2009,1),frequency = 12) ->IMAETS

IMAETS %>% head(10)

##          Costa Rica El Salvador Guatemala Honduras Nicaragua Panama
## Jan 2009      73.05       86.73     86.65   157.26    105.02 195.71
## Feb 2009      70.50       80.85     84.95   159.33     99.61 189.71
## Mar 2009      75.68       87.19     90.17   169.91     99.82 204.64
## Apr 2009      70.13       83.92     87.79   156.18     97.86 188.06
## May 2009      72.30       91.42     85.69   164.17    105.33 193.65
## Jun 2009      73.43       93.46     83.92   163.04    102.92 199.60
## Jul 2009      72.93       86.39     87.09   155.42    111.29 188.20
## Aug 2009      72.00       86.72     85.90   159.89    106.11 190.96
## Sep 2009      73.64       87.57     84.65   157.82    100.80 195.45
## Oct 2009      76.77       85.27     87.06   166.33    103.08 204.89

autoplot(IMAETS,xlab = "años",ylab = "Indice",main = "IMAE de Centroamerica y Panama 2009-2022 (Septiembre)") + theme_bw()

Proyección a seis meses

modeloCR<-auto.arima(y=IMAETS[,1])
modeloES<-auto.arima(y=IMAETS[,2])
modeloGT<-auto.arima(y=IMAETS[,3])
modeloHN<-auto.arima(y=IMAETS[,4])
modeloNC<-auto.arima(y=IMAETS[,5])
modeloPA<-auto.arima(y=IMAETS[,6])

prCR<-forecast(modeloCR,h=6)
prES<-forecast(modeloES,h=6)
prGT<-forecast(modeloGT,h=6)
prHN<-forecast(modeloHN,h=6)
prNC<-forecast(modeloNC,h=6)
prPA<-forecast(modeloPA,h=6)

Costa Rica

autoplot(prCR)+xlab("Años")+ylab("Indice")+theme_bw()

autoplot(prCR$x,series = "IMAE")+autolayer(prCR$fitted,series = "Pronostico")+ggtitle("Ajuste SARIMA Costa Rica")

El Salvador

autoplot(prES)+xlab("Años")+ylab("Indice")+theme_bw()

autoplot(prES$x,series = "IMAE")+autolayer(prES$fitted,series = "Pronostico")+ggtitle("Ajuste SARIMA El Salvador")

Guatemala

autoplot(prGT)+xlab("Años")+ylab("Indice")+theme_bw()

autoplot(prGT$x,series = "IMAE")+autolayer(prGT$fitted,series = "Pronostico")+ggtitle("Ajuste SARIMA Guatemala")

Honduras

autoplot(prHN)+xlab("Años")+ylab("Indice")+theme_bw()

autoplot(prHN$x,series = "IMAE")+autolayer(prHN$fitted,series = "Pronostico")+ggtitle("Ajuste SARIMA Honduras")

Nicaragua

autoplot(prNC)+xlab("Años")+ylab("Indice")+theme_bw()

autoplot(prNC$x,series = "IMAE")+autolayer(prNC$fitted,series = "Pronostico")+ggtitle("Ajuste SARIMA Nicaragua")

Panamá

autoplot(prPA)+xlab("Años")+ylab("Indice")+theme_bw()

autoplot(prPA$x,series = "IMAE")+autolayer(prPA$fitted,series = "Pronostico")+ggtitle("Ajuste SARIMA Costa Rica")

Pronosticos

library(TSstudio)
union<-cbind(prCR$fitted,prES$fitted,prGT$fitted,prHN$fitted,prNC$fitted,prPA$fitted)
ts_plot(union)

Comparativo

library(TSstudio)
comparativo<-cbind(prCR$x,prCR$fitted,prES$x,prES$fitted,prGT$x,prGT$fitted,prHN$x,prHN$fitted,prNC$x,prNC$fitted,prPA$x,prPA$fitted)
ts_plot(comparativo)

Serie Ampliada

Costa Rica

CR<-ts(as.numeric(rbind(as.matrix(prCR$x),as.matrix(prCR$mean))),start = c(2009,1),frequency = 12)

CR

##           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 108.5389 110.5827 117.3577                                             
##           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 110.4800 113.9698 118.1828 120.7984
## 2023

El Salvador

ES<-ts(as.numeric(rbind(as.matrix(prES$x),as.matrix(prES$mean))),start = c(2009,1),frequency = 12)

ES

##           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 114.0090 112.4458 118.1053                                             
##           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 116.3000 113.8595 119.5214 126.4813
## 2023

Guatemala

GT<-ts(as.numeric(rbind(as.matrix(prGT$x),as.matrix(prGT$mean))),start = c(2009,1),frequency = 12)

GT

##           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 137.9819 137.2163 140.3554                                             
##           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 133.9700 135.3104 138.3894 143.2999
## 2023

Honduras

HN<-ts(as.numeric(rbind(as.matrix(prHN$x),as.matrix(prHN$mean))),start = c(2009,1),frequency = 12)

HN

##           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 247.4000 246.6600 263.7900 248.8500 254.8200 256.3800 246.2100 272.0500
## 2023 257.6585 258.5383 266.7452                                             
##           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 259.3600 273.1095 265.5545 286.4530
## 2023

Nicaragua

NC<-ts(as.numeric(rbind(as.matrix(prNC$x),as.matrix(prNC$mean))),start = c(2009,1),frequency = 12)

NC

##           Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 2009 105.0200  99.6100  99.8200  97.8600 105.3300 102.9200 111.2900 106.1100
## 2010 107.2200 102.0400 106.1700 100.2500 108.4700 107.9800 116.4400 110.7000
## 2011 112.8400 105.4000 114.8900 106.1900 118.5800 116.4600 126.3600 118.6100
## 2012 128.3900 116.8500 118.6400 112.5100 126.3100 118.1000 130.2900 123.8800
## 2013 132.0700 122.4000 122.3000 126.7600 132.7900 123.1800 138.3600 130.1900
## 2014 135.6700 129.8000 132.0300 128.8600 139.0400 130.0300 143.7300 133.0500
## 2015 141.7300 135.0600 139.1000 131.3200 143.7100 134.6900 151.2900 141.6700
## 2016 148.0100 141.7300 143.0000 140.8700 153.1300 144.2400 155.8000 149.6600
## 2017 159.9000 150.2100 154.6600 144.2100 159.9800 150.5200 161.8600 154.3900
## 2018 165.6100 154.2000 158.4100 150.6200 151.5600 130.5400 153.2300 148.9800
## 2019 151.8100 138.1100 139.7100 137.9200 145.1900 135.0100 150.3200 143.5600
## 2020 153.2600 145.1100 140.7000 124.9300 134.7700 130.1500 148.6100 139.3900
## 2021 155.5500 148.1500 152.2400 145.9700 159.5200 155.1000 165.8600 154.9200
## 2022 166.4600 154.5400 161.0500 153.4500 167.0000 159.8800 171.4700 162.1400
## 2023 170.0220 160.4747 163.3884                                             
##           Sep      Oct      Nov      Dec
## 2009 100.8000 103.0800 109.2500 120.2100
## 2010 106.3500 110.0700 116.5600 124.6700
## 2011 112.8200 113.7400 125.9000 128.3200
## 2012 117.0800 126.1900 130.7100 142.1100
## 2013 125.1200 130.0500 134.0200 147.2900
## 2014 131.2400 137.4900 141.3800 157.0800
## 2015 141.0100 146.6000 148.6300 163.1400
## 2016 143.5700 149.0700 155.8500 171.4100
## 2017 147.5700 154.8200 164.8600 176.5600
## 2018 141.0600 143.0700 153.8200 165.2800
## 2019 138.8200 147.6200 154.0100 165.5900
## 2020 140.7700 148.1000 145.8600 164.7600
## 2021 151.3800 160.5600 165.8900 178.7400
## 2022 154.9700 163.0803 168.2459 181.6495
## 2023

Panamá

PA<-ts(as.numeric(rbind(as.matrix(prPA$x),as.matrix(prPA$mean))),start = c(2009,1),frequency = 12)

PA

##           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 304.6900 322.9200 355.1200 307.2600 314.8500 310.0800 306.6400 319.0100
## 2022 354.2100 368.6200 390.5100 334.9700 344.4200 348.0300 317.4900 360.2300
## 2023 378.9082 388.8002 410.8596                                             
##           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 318.0600 344.2300 332.5600 396.0500
## 2022 356.9600 381.0015 369.5564 412.5766
## 2023

Descomposición temporal

Costa Rica

ts_decompose(CR, type = "additive", showline = TRUE)

library(stats)
fitCR<-stl(CR,"periodic")
TCCR<-fitCR$time.series[,2]
TCCR

##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2009  73.49623  73.61103  73.72584  73.84305  73.96026  74.08960  74.21893
## 2010  75.56195  75.89689  76.23184  76.55175  76.87166  77.11590  77.36013
## 2011  78.63026  78.90632  79.18239  79.48676  79.79114  80.10348  80.41582
## 2012  82.17225  82.44060  82.70896  82.94258  83.17620  83.33039  83.48458
## 2013  84.20802  84.44128  84.67455  84.90429  85.13404  85.36738  85.60071
## 2014  86.87680  87.08175  87.28670  87.54434  87.80199  88.07168  88.34138
## 2015  90.79669  91.34148  91.88626  92.34790  92.80954  93.19650  93.58347
## 2016  95.28402  95.56406  95.84410  96.22755  96.61099  96.97056  97.33012
## 2017  98.74610  98.91413  99.08216  99.28649  99.49082  99.76547 100.04012
## 2018 101.95599 102.29784 102.63969 102.84284 103.04600 103.18399 103.32198
## 2019 103.65880 103.77934 103.89987 104.09372 104.28756 104.48074 104.67393
## 2020 101.93479 101.06362 100.19245  99.50019  98.80793  98.45018  98.09244
## 2021 102.02720 103.05190 104.07659 105.00632 105.93605 106.76990 107.60375
## 2022 110.65032 110.86943 111.08854 111.25684 111.42513 111.57356 111.72198
## 2023 112.87662 113.08235 113.28809                                        
##            Aug       Sep       Oct       Nov       Dec
## 2009  74.37561  74.53230  74.75538  74.97846  75.27020
## 2010  77.54315  77.72618  77.93566  78.14514  78.38770
## 2011  80.72948  81.04313  81.34108  81.63902  81.90564
## 2012  83.55125  83.61792  83.73136  83.84479  84.02640
## 2013  85.85098  86.10126  86.31890  86.53655  86.70667
## 2014  88.63204  88.92271  89.33594  89.74918  90.27294
## 2015  93.92490  94.26634  94.55107  94.83581  95.05991
## 2016  97.58036  97.83059  98.07082  98.31104  98.52857
## 2017 100.29855 100.55698 100.87001 101.18305 101.56952
## 2018 103.42381 103.52565 103.55486 103.58407 103.62144
## 2019 104.64510 104.61627 104.10928 103.60229 102.76854
## 2020  98.26294  98.43344  99.18844  99.94343 100.98532
## 2021 108.31461 109.02546 109.50710 109.98874 110.31953
## 2022 111.90155 112.08111 112.27589 112.47066 112.67364
## 2023

El Salvador

ts_decompose(ES, type = "additive", showline = TRUE)

fitES<-stl(ES,"periodic")
TCES<-fitES$time.series[,2]
TCES

##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2009  87.44831  87.61030  87.77228  87.93043  88.08857  88.25137  88.41417
## 2010  89.39892  89.55276  89.70660  89.87017  90.03374  90.22404  90.41434
## 2011  92.44197  92.80097  93.15997  93.40055  93.64113  93.85085  94.06057
## 2012  95.62211  95.95591  96.28971  96.54743  96.80514  96.94480  97.08446
## 2013  97.36930  97.57401  97.77872  98.01214  98.24556  98.50016  98.75475
## 2014  99.97560 100.08628 100.19697 100.19462 100.19226 100.13260 100.07294
## 2015 100.61538 100.89979 101.18421 101.48466 101.78512 101.98925 102.19338
## 2016 103.30998 103.67619 104.04240 104.37260 104.70281 104.96198 105.22115
## 2017 105.98738 106.19378 106.40019 106.64078 106.88137 107.13670 107.39202
## 2018 108.53248 108.78850 109.04453 109.27738 109.51023 109.72039 109.93055
## 2019 111.20149 111.44740 111.69331 111.95718 112.22105 112.39619 112.57134
## 2020 106.56790 105.30867 104.04944 103.38762 102.72580 102.59519 102.46457
## 2021 110.00302 110.88111 111.75920 112.26223 112.76525 113.08199 113.39873
## 2022 114.86436 115.08678 115.30920 115.57679 115.84438 116.07178 116.29919
## 2023 117.21726 117.35708 117.49691                                        
##            Aug       Sep       Oct       Nov       Dec
## 2009  88.59371  88.77325  88.95216  89.13107  89.26499
## 2010  90.66320  90.91205  91.25929  91.60654  92.02426
## 2011  94.27028  94.47999  94.72729  94.97458  95.29835
## 2012  97.10093  97.11740  97.14201  97.16662  97.26796
## 2013  99.00061  99.24647  99.44571  99.64496  99.81028
## 2014 100.05181 100.03068 100.12122 100.21176 100.41357
## 2015 102.28983 102.38628 102.54564 102.70501 103.00750
## 2016 105.37282 105.52449 105.61107 105.69766 105.84252
## 2017 107.59687 107.80171 107.96678 108.13185 108.33216
## 2018 110.15652 110.38250 110.58853 110.79456 110.99802
## 2019 112.33895 112.10655 110.95546 109.80436 108.18613
## 2020 103.18347 103.90236 105.45761 107.01285 108.50793
## 2021 113.63737 113.87601 114.12445 114.37289 114.61862
## 2022 116.45827 116.61735 116.77020 116.92305 117.07015
## 2023

Guatemala

ts_decompose(GT, type = "additive", showline = TRUE)

fitGT<-stl(GT,"periodic")
TCGT<-fitGT$time.series[,2]
TCGT

##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2009  86.06129  86.27892  86.49655  86.70443  86.91230  87.11913  87.32597
## 2010  88.53167  88.71821  88.90476  89.11966  89.33456  89.59635  89.85814
## 2011  91.78800  92.20524  92.62247  92.98003  93.33759  93.61300  93.88842
## 2012  95.24801  95.49523  95.74245  96.01742  96.29239  96.56241  96.83243
## 2013  98.52919  98.83917  99.14916  99.43305  99.71695  99.97579 100.23463
## 2014 102.24919 102.65525 103.06131 103.46417 103.86703 104.26639 104.66576
## 2015 106.68770 107.10443 107.52117 107.90686 108.29256 108.56870 108.84484
## 2016 110.11261 110.33241 110.55220 110.83620 111.12021 111.49918 111.87815
## 2017 113.84974 114.12818 114.40662 114.61404 114.82146 115.00388 115.18630
## 2018 117.20538 117.60032 117.99525 118.33719 118.67913 118.96821 119.25729
## 2019 121.38235 121.83153 122.28072 122.72953 123.17835 123.55066 123.92297
## 2020 121.49607 121.07629 120.65651 120.62134 120.58617 120.91099 121.23580
## 2021 127.78761 128.59039 129.39317 129.96080 130.52842 131.02954 131.53065
## 2022 134.24207 134.65968 135.07729 135.43940 135.80152 136.03393 136.26633
## 2023 137.15706 137.27954 137.40203                                        
##            Aug       Sep       Oct       Nov       Dec
## 2009  87.53758  87.74919  87.94115  88.13311  88.33239
## 2010  90.13509  90.41205  90.71710  91.02214  91.40507
## 2011  94.12665  94.36487  94.58231  94.79975  95.02388
## 2012  97.08568  97.33894  97.62283  97.90672  98.21795
## 2013 100.50652 100.77840 101.11729 101.45618 101.85268
## 2014 105.01319 105.36063 105.66678 105.97292 106.33031
## 2015 109.04041 109.23598 109.45280 109.66961 109.89111
## 2016 112.25857 112.63899 112.95127 113.26354 113.55664
## 2017 115.43389 115.68149 116.03494 116.38840 116.79689
## 2018 119.55196 119.84663 120.19984 120.55304 120.96770
## 2019 123.95542 123.98787 123.46096 122.93405 122.21506
## 2020 122.05656 122.87733 124.14347 125.40962 126.59862
## 2021 132.00831 132.48597 132.93681 133.38764 133.81486
## 2022 136.42481 136.58328 136.73331 136.88334 137.02020
## 2023

Honduras

ts_decompose(HN, type = "additive", showline = TRUE)

fitHN<-stl(HN,"periodic")
TCHN<-fitHN$time.series[,2]
TCHN

##           Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 2009 162.7922 162.8714 162.9506 163.0758 163.2010 163.4149 163.6288 163.9173
## 2010 167.0258 168.0503 169.0748 169.9932 170.9117 171.6374 172.3630 173.0517
## 2011 177.8866 179.1907 180.4948 181.5848 182.6749 183.3871 184.0992 184.6501
## 2012 188.4346 189.3986 190.3626 191.2927 192.2229 192.7749 193.3269 193.4740
## 2013 194.8123 195.2192 195.6261 196.0270 196.4279 196.8313 197.2346 197.5553
## 2014 199.5782 200.0875 200.5969 201.0094 201.4219 201.7717 202.1215 202.4741
## 2015 205.0898 205.8918 206.6937 207.4616 208.2294 208.8786 209.5277 210.0408
## 2016 213.0171 213.7077 214.3982 215.1428 215.8873 216.7646 217.6419 218.5715
## 2017 223.3705 224.4037 225.4368 226.2772 227.1176 227.7519 228.3862 228.9496
## 2018 232.1603 232.9082 233.6562 234.4374 235.2187 236.0089 236.7990 237.4897
## 2019 240.4421 241.0163 241.5904 242.1852 242.7799 243.4230 244.0662 243.7384
## 2020 232.0458 229.5214 226.9971 225.2656 223.5342 222.7506 221.9670 223.2894
## 2021 237.1843 239.4768 241.7693 243.9815 246.1936 248.2292 250.2648 251.6071
## 2022 256.2563 256.9722 257.6880 258.4280 259.1680 259.9001 260.6321 261.1837
## 2023 263.9071 264.3523 264.7975                                             
##           Sep      Oct      Nov      Dec
## 2009 164.2057 164.7116 165.2175 166.1217
## 2010 173.7404 174.6408 175.5412 176.7139
## 2011 185.2010 185.9228 186.6445 187.5396
## 2012 193.6212 193.8204 194.0196 194.4159
## 2013 197.8759 198.2412 198.6064 199.0923
## 2014 202.8267 203.2901 203.7535 204.4216
## 2015 210.5538 211.0984 211.6430 212.3301
## 2016 219.5012 220.4223 221.3434 222.3569
## 2017 229.5130 230.1221 230.7312 231.4457
## 2018 238.1803 238.7557 239.3310 239.8866
## 2019 243.4107 240.9849 238.5592 235.3025
## 2020 224.6118 227.8167 231.0216 234.1030
## 2021 252.9494 253.8615 254.7737 255.5150
## 2022 261.7353 262.2962 262.8571 263.3821
## 2023

Nicaragua

ts_decompose(NC, type = "additive", showline = TRUE)

fitNC<-stl(NC,"periodic")
TCNC<-fitNC$time.series[,2]
TCNC

##           Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 2009 103.6888 103.9761 104.2634 104.5188 104.7743 105.0221 105.2700 105.5289
## 2010 106.8890 107.3958 107.9027 108.5235 109.1443 109.6640 110.1838 110.6002
## 2011 113.3426 114.0843 114.8260 115.5525 116.2790 117.0044 117.7298 118.3565
## 2012 120.6043 121.1237 121.6432 122.3218 123.0004 123.6505 124.3007 124.8360
## 2013 127.7857 128.3793 128.9729 129.4556 129.9384 130.3405 130.7426 131.1472
## 2014 133.4116 133.9410 134.4704 135.0660 135.6615 136.2490 136.8364 137.2963
## 2015 139.5126 140.1672 140.8218 141.5301 142.2384 142.8709 143.5034 144.0509
## 2016 146.9618 147.4524 147.9430 148.4236 148.9042 149.5754 150.2465 150.9714
## 2017 154.0134 154.5026 154.9918 155.4243 155.8568 156.2837 156.7106 157.1127
## 2018 156.4358 155.5542 154.6727 153.6290 152.5854 151.4071 150.2289 149.0981
## 2019 145.8695 145.6277 145.3859 145.3228 145.2597 145.4546 145.6495 145.8687
## 2020 144.4481 143.9674 143.4867 143.1463 142.8059 142.7930 142.7802 143.5240
## 2021 150.7926 152.1960 153.5995 154.8287 156.0580 157.1375 158.2171 158.9668
## 2022 161.8994 162.3566 162.8139 163.1153 163.4168 163.6601 163.9034 164.1435
## 2023 165.2180 165.4175 165.6169                                             
##           Sep      Oct      Nov      Dec
## 2009 105.7878 106.0063 106.2247 106.5569
## 2010 111.0166 111.5042 111.9918 112.6672
## 2011 118.9831 119.4093 119.8354 120.2199
## 2012 125.3713 125.9659 126.5605 127.1731
## 2013 131.5517 131.9951 132.4385 132.9250
## 2014 137.7562 138.1486 138.5409 139.0267
## 2015 144.5985 145.1856 145.7727 146.3673
## 2016 151.6963 152.3107 152.9250 153.4692
## 2017 157.5147 157.5613 157.6080 157.0219
## 2018 147.9673 147.2344 146.5015 146.1855
## 2019 146.0879 145.8328 145.5776 145.0128
## 2020 144.2679 145.8113 147.3548 149.0737
## 2021 159.7165 160.2807 160.8449 161.3721
## 2022 164.3837 164.5992 164.8147 165.0164
## 2023

Panamá

ts_decompose(PA, type = "additive", showline = TRUE)

fitPA<-stl(PA,"periodic")
TCPA<-fitPA$time.series[,2]
TCPA

##           Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 2009 192.4250 192.9457 193.4664 193.8701 194.2737 194.6665 195.0592 195.5925
## 2010 199.1105 200.4626 201.8147 203.2741 204.7335 205.9308 207.1281 207.8802
## 2011 213.2975 215.1673 217.0371 219.1348 221.2326 223.1746 225.1166 226.6557
## 2012 235.3312 237.5340 239.7368 241.7028 243.6689 245.2248 246.7807 247.8856
## 2013 254.6455 256.7656 258.8858 261.1751 263.4644 265.1798 266.8953 267.6956
## 2014 271.1296 272.4178 273.7060 275.2419 276.7779 278.2995 279.8212 280.8541
## 2015 284.3676 285.4071 286.4466 287.7051 288.9636 290.1572 291.3508 292.2493
## 2016 296.4126 297.5872 298.7619 300.1363 301.5108 302.8744 304.2379 305.5393
## 2017 312.0116 313.3666 314.7217 316.1185 317.5152 318.7182 319.9212 320.6400
## 2018 322.1074 322.4547 322.8019 323.2495 323.6971 324.2484 324.7997 325.3510
## 2019 328.5259 329.7091 330.8923 332.1799 333.4674 334.5641 335.6608 335.6901
## 2020 313.0943 304.9582 296.8221 290.7814 284.7408 281.6620 278.5832 279.4310
## 2021 303.7917 308.9223 314.0529 317.7370 321.4211 324.9114 328.4017 332.1991
## 2022 348.4373 350.8030 353.1686 355.2583 357.3479 359.8415 362.3351 365.2086
## 2023 380.7405 384.2617 387.7829                                             
##           Sep      Oct      Nov      Dec
## 2009 196.1258 196.7394 197.3530 198.2317
## 2010 208.6323 209.5888 210.5452 211.9214
## 2011 228.1949 229.7969 231.3988 233.3650
## 2012 248.9906 250.1916 251.3926 253.0190
## 2013 268.4960 268.9784 269.4609 270.2952
## 2014 281.8871 282.4462 283.0053 283.6865
## 2015 293.1477 293.8633 294.5789 295.4957
## 2016 306.8406 308.1058 309.3709 310.6913
## 2017 321.3587 321.5547 321.7506 321.9290
## 2018 325.9024 326.4219 326.9414 327.7337
## 2019 335.7194 331.9022 328.0850 320.5897
## 2020 280.2788 285.4022 290.5255 297.1586
## 2021 335.9964 339.4297 342.8630 345.6501
## 2022 368.0820 371.1074 374.1327 377.4366
## 2023

Calculo de las tasas

Costa Rica

library(dplyr)
library(zoo)
TCCR %>% as.numeric() %>% as.data.frame()->TC_df
names(TC_df)<-c("TC")
TC_df %>% mutate(T_1_1=(TC/dplyr::lag(TC,n=1)-1)*100,
                 T_1_12=(TC/dplyr::lag(TC,n=12)-1)*100,
                 T_12_12=(rollapply(TC,12,mean,align='right',fill=NA)/rollapply(dplyr::lag(TC,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)->tabla_coyuntura1
print(tail(tabla_coyuntura1,n=12))

##                TC     T_1_1   T_1_12  T_12_12  T_1_12C T_12_12C
## Apr 2022 111.2568 0.1514999 5.952514 8.704458 2.528406       NA
## May 2022 111.4251 0.1512707 5.181510 8.518254 2.256520       NA
## Jun 2022 111.5736 0.1332048 4.499075 8.172465 2.133900       NA
## Jul 2022 111.7220 0.1330276 3.827216 7.671330 2.012013       NA
## Aug 2022 111.9015 0.1607250 3.311593 7.088425 1.995973       NA
## Sep 2022 112.0811 0.1604671 2.802693 6.426472 1.979996       NA
## Oct 2022 112.2759 0.1737787 2.528406 5.775807       NA       NA
## Nov 2022 112.4707 0.1734772 2.256520 5.135735       NA       NA
## Dec 2022 112.6736 0.1804743 2.133900 4.553702       NA       NA
## Jan 2023 112.8766 0.1801492 2.012013 4.027487       NA       NA
## Feb 2023 113.0824 0.1822634 1.995973 3.571466       NA       NA
## Mar 2023 113.2881 0.1819318 1.979996 3.183534       NA       NA

El Salvador

library(dplyr)
library(zoo)
TCES %>% as.numeric() %>% as.data.frame()->TC_df
names(TC_df)<-c("TC")
TC_df %>% mutate(T_1_1=(TC/dplyr::lag(TC,n=1)-1)*100,
                 T_1_12=(TC/dplyr::lag(TC,n=12)-1)*100,
                 T_12_12=(rollapply(TC,12,mean,align='right',fill=NA)/rollapply(dplyr::lag(TC,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)->tabla_coyuntura2
print(tail(tabla_coyuntura2,n=12))

##                TC     T_1_1   T_1_12  T_12_12  T_1_12C T_12_12C
## Apr 2022 115.5768 0.2320654 2.952518 7.023752 2.318303       NA
## May 2022 115.8444 0.2315281 2.730568 6.429896 2.229694       NA
## Jun 2022 116.0718 0.1962999 2.643919 5.801954 2.138859       NA
## Jul 2022 116.2992 0.1959153 2.557755 5.141385 2.048413       NA
## Aug 2022 116.4583 0.1367854 2.482369 4.523659 1.972689       NA
## Sep 2022 116.6173 0.1365985 2.407299 3.947100 1.897257       NA
## Oct 2022 116.7702 0.1310721 2.318303 3.472699       NA       NA
## Nov 2022 116.9231 0.1309005 2.229694 3.097110       NA       NA
## Dec 2022 117.0702 0.1258107 2.138859 2.813048       NA       NA
## Jan 2023 117.2173 0.1256526 2.048413 2.618497       NA       NA
## Feb 2023 117.3571 0.1192878 1.972689 2.468518       NA       NA
## Mar 2023 117.4969 0.1191456 1.897257 2.362483       NA       NA

Guatemala

library(dplyr)
library(zoo)
TCGT %>% as.numeric() %>% as.data.frame()->TC_df
names(TC_df)<-c("TC")
TC_df %>% mutate(T_1_1=(TC/dplyr::lag(TC,n=1)-1)*100,
                 T_1_12=(TC/dplyr::lag(TC,n=12)-1)*100,
                 T_12_12=(rollapply(TC,12,mean,align='right',fill=NA)/rollapply(dplyr::lag(TC,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)->tabla_coyuntura3
print(tail(tabla_coyuntura3,n=12))

##                TC      T_1_1   T_1_12  T_12_12  T_1_12C T_12_12C
## Apr 2022 135.4394 0.26808085 4.215585 6.507957 2.855871       NA
## May 2022 135.8015 0.26736409 4.039809 6.155774 2.620706       NA
## Jun 2022 136.0339 0.17113588 3.819285 5.778241 2.395356       NA
## Jul 2022 136.2663 0.17084351 3.600441 5.375992 2.171441       NA
## Aug 2022 136.4248 0.11629904 3.345620 4.981783 1.945544       NA
## Sep 2022 136.5833 0.11616394 3.092637 4.595197 1.721043       NA
## Oct 2022 136.7333 0.10984309 2.855871 4.248599       NA       NA
## Nov 2022 136.8833 0.10972257 2.620706 3.940774       NA       NA
## Dec 2022 137.0202 0.09998366 2.395356 3.667790       NA       NA
## Jan 2023 137.1571 0.09988379 2.171441 3.428776       NA       NA
## Feb 2023 137.2795 0.08930141 1.945544 3.198159       NA       NA
## Mar 2023 137.4020 0.08922174 1.721043 2.975677       NA       NA

Honduras

library(dplyr)
library(zoo)
TCHN %>% as.numeric() %>% as.data.frame()->TC_df
names(TC_df)<-c("TC")
TC_df %>% mutate(T_1_1=(TC/dplyr::lag(TC,n=1)-1)*100,
                 T_1_12=(TC/dplyr::lag(TC,n=12)-1)*100,
                 T_12_12=(rollapply(TC,12,mean,align='right',fill=NA)/rollapply(dplyr::lag(TC,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)->tabla_coyuntura4
print(tail(tabla_coyuntura4,n=12))

##                TC     T_1_1   T_1_12  T_12_12  T_1_12C T_12_12C
## Apr 2022 258.4280 0.2871535 5.921169 9.786473 3.322545       NA
## May 2022 259.1680 0.2863313 5.269987 9.360492 3.172769       NA
## Jun 2022 259.9001 0.2824766 4.701657 8.786212 3.078920       NA
## Jul 2022 260.6321 0.2816809 4.142571 8.069319 2.985614       NA
## Aug 2022 261.1837 0.2116335 3.806202 7.338298 2.871965       NA
## Sep 2022 261.7353 0.2111865 3.473402 6.593576 2.758948       NA
## Oct 2022 262.2962 0.2142879 3.322545 5.934081       NA       NA
## Nov 2022 262.8571 0.2138296 3.172769 5.356023       NA       NA
## Dec 2022 263.3821 0.1997370 3.078920 4.862316       NA       NA
## Jan 2023 263.9071 0.1993388 2.985614 4.449995       NA       NA
## Feb 2023 264.3523 0.1687016 2.871965 4.088361       NA       NA
## Mar 2023 264.7975 0.1684175 2.758948 3.775971       NA       NA

Nicaragua

library(dplyr)
library(zoo)
TCNC %>% as.numeric() %>% as.data.frame()->TC_df
names(TC_df)<-c("TC")
TC_df %>% mutate(T_1_1=(TC/dplyr::lag(TC,n=1)-1)*100,
                 T_1_12=(TC/dplyr::lag(TC,n=12)-1)*100,
                 T_12_12=(rollapply(TC,12,mean,align='right',fill=NA)/rollapply(dplyr::lag(TC,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)->tabla_coyuntura5
print(tail(tabla_coyuntura5,n=12))

##                TC     T_1_1   T_1_12  T_12_12  T_1_12C T_12_12C
## Apr 2022 163.1153 0.1851380 5.352124 8.642151 2.694363       NA
## May 2022 163.4168 0.1847959 4.715434 8.247410 2.468143       NA
## Jun 2022 163.6601 0.1488800 4.150832 7.746413 2.258311       NA
## Jul 2022 163.9034 0.1486586 3.593935 7.142587 2.049846       NA
## Aug 2022 164.1435 0.1465200 3.256474 6.520751 1.885257       NA
## Sep 2022 164.3837 0.1463056 2.922182 5.881363 1.721593       NA
## Oct 2022 164.5992 0.1311213 2.694363 5.289303       NA       NA
## Nov 2022 164.8147 0.1309496 2.468143 4.742544       NA       NA
## Dec 2022 165.0164 0.1223517 2.258311 4.252238       NA       NA
## Jan 2023 165.2180 0.1222022 2.049846 3.816362       NA       NA
## Feb 2023 165.4175 0.1207037 1.885257 3.423373       NA       NA
## Mar 2023 165.6169 0.1205581 1.721593 3.072006       NA       NA

Panamá

library(dplyr)
library(zoo)
TCPA %>% as.numeric() %>% as.data.frame()->TC_df
names(TC_df)<-c("TC")
TC_df %>% mutate(T_1_1=(TC/dplyr::lag(TC,n=1)-1)*100,
                 T_1_12=(TC/dplyr::lag(TC,n=12)-1)*100,
                 T_12_12=(rollapply(TC,12,mean,align='right',fill=NA)/rollapply(dplyr::lag(TC,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)->tabla_coyuntura6
print(tail(tabla_coyuntura6,n=12))

##                TC     T_1_1    T_1_12   T_12_12  T_1_12C T_12_12C
## Apr 2022 355.2583 0.5916849 11.808910 15.792406 9.332630       NA
## May 2022 357.3479 0.5882046 11.177493 15.608470 9.120196       NA
## Jun 2022 359.8415 0.6978059 10.750652 15.190118 9.196144       NA
## Jul 2022 362.3351 0.6929703 10.332885 14.547938 9.270876       NA
## Aug 2022 365.2086 0.7930363  9.936667 13.807407 9.537752       NA
## Sep 2022 368.0820 0.7867967  9.549405 12.974410 9.801052       NA
## Oct 2022 371.1074 0.8219244  9.332630 12.204737       NA       NA
## Nov 2022 374.1327 0.8152239  9.120196 11.494726       NA       NA
## Dec 2022 377.4366 0.8830830  9.196144 10.925712       NA       NA
## Jan 2023 380.7405 0.8753529  9.270876 10.490859       NA       NA
## Feb 2023 384.2617 0.9248224  9.537752 10.170621       NA       NA
## Mar 2023 387.7829 0.9163478  9.801052  9.960778       NA       NA

Grafico de las tasas centradas

Costa Rica

tabla_coyuntura1 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->CRg

ts_plot(CRg,title = "Tasas centradas Costa Rica",Xtitle = "Años",Ytitle = "Tasas")

tabla_coyuntura1 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% ts_plot(title = "T_1_1 Costa Rica",Xtitle = "Años",Ytitle = "T_1_1")

El Salvador

tabla_coyuntura2 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->ESg

ts_plot(ESg,title = "Tasas centradas El Salvador",Xtitle = "Años",Ytitle = "Tasas")

tabla_coyuntura2 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% ts_plot(title = "T_1_1 El Salvador",Xtitle = "Años",Ytitle = "T_1_1")

Guatemala

tabla_coyuntura3 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->GTg

ts_plot(GTg,title = "Tasas centradas Guatemala",Xtitle = "Años",Ytitle = "Tasas")

tabla_coyuntura3 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% ts_plot(title = "T_1_1 Guatemala",Xtitle = "Años",Ytitle = "T_1_1")

Honduras

tabla_coyuntura4 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->HNg

ts_plot(HNg,title = "Tasas centradas Honduras",Xtitle = "Años",Ytitle = "Tasas")

tabla_coyuntura4 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% ts_plot(title = "T_1_1 Honduras",Xtitle = "Años",Ytitle = "T_1_1")

Nicaragua

tabla_coyuntura5 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->NCg

ts_plot(NCg,title = "Tasas centradas Nicaragua",Xtitle = "Años",Ytitle = "Tasas")

tabla_coyuntura5 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% ts_plot(title = "T_1_1 Nicaragua",Xtitle = "Años",Ytitle = "T_1_1")

Panamá

tabla_coyuntura6 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->PAg

ts_plot(PAg,title = "Tasas centradas Panamá",Xtitle = "Años",Ytitle = "Tasas")

tabla_coyuntura6 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% ts_plot(title = "T_1_1 Panamá",Xtitle = "Años",Ytitle = "T_1_1")

Paquetes utilizados

Hyndman, Rob J, and Yeasmin Khandakar. 2008. “Automatic Time Series Forecasting: The Forecast Package for R.” Journal of Statistical Software 26 (3): 1–22. https://doi.org/10.18637/jss.v027.i03.

Hyndman, Rob, George Athanasopoulos, Christoph Bergmeir, Gabriel Caceres, Leanne Chhay, Kirill Kuroptev, Mitchell O’Hara-Wild, et al. 2022. Forecast: Forecasting Functions for Time Series and Linear Models. https://CRAN.R-project.org/package=forecast.

Krispin, Rami. 2020. TSstudio: Functions for Time Series Analysis and Forecasting. https://github.com/RamiKrispin/TSstudio.

R Core Team. 2022. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

Wickham, Hadley, and Jennifer Bryan. 2022. Readxl: Read Excel Files. https://CRAN.R-project.org/package=readxl.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2022. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.

Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2022. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.

Zeileis, Achim, and Gabor Grothendieck. 2005. “Zoo: S3 Infrastructure for Regular and Irregular Time Series.” Journal of Statistical Software 14 (6): 1–27. https://doi.org/10.18637/jss.v014.i06.

Zeileis, Achim, Gabor Grothendieck, and Jeffrey A. Ryan. 2022. Zoo: S3 Infrastructure for Regular and Irregular Time Series (z’s Ordered Observations). https://zoo.R-Forge.R-project.org/.