EL SALVADOR
1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/Usuario/Desktop/REBE/ivae_ts/ivae_ts.xlsx",
skip = 4)
ivae_ts$`El Salvador` %>% ts(start = c(2009,1),
frequency = 12)->ivae
print(ivae)
## 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 0.00
## 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,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2009-2022 (agosto)")+theme_bw()
2. proyección a Seis meses
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)
## Series: ivae
## ARIMA(2,1,1)(0,0,2)[12]
##
## Coefficients:
## ar1 ar2 ma1 sma1 sma2
## -1.0279 -0.5165 0.9047 0.5254 0.1848
## s.e. 0.1864 0.1838 0.0574 0.1857 0.1345
##
## sigma^2 = 95.01: log likelihood = -605.91
## AIC=1223.82 AICc=1224.36 BIC=1242.42
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -0.5533854 9.568672 3.713414 -Inf Inf 0.8152916 0.01002908
pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("Años")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "IVAE")+autolayer(pronosticos$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
3. Serie ampliada
ivae_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2009,1),frequency = 12)
print(ivae_h)
## Jan Feb Mar Apr May Jun Jul
## 2009 86.73000 80.85000 87.19000 83.92000 91.42000 93.46000 86.39000
## 2010 85.56000 84.69000 90.90000 85.94000 94.33000 92.23000 87.18000
## 2011 90.27000 86.73000 94.32000 90.79000 98.50000 97.59000 92.16000
## 2012 92.65000 91.20000 98.46000 91.23000 102.83000 102.84000 93.61000
## 2013 95.67000 90.77000 96.12000 96.34000 103.08000 101.58000 96.42000
## 2014 98.70000 94.70000 101.30000 97.12000 103.86000 104.73000 98.48000
## 2015 98.87000 94.82000 103.15000 98.75000 105.65000 105.45000 101.67000
## 2016 99.25000 97.76000 102.58000 103.43000 107.76000 110.71000 104.01000
## 2017 101.41000 98.97000 108.44000 101.40000 110.85000 113.63000 105.51000
## 2018 105.17000 102.53000 108.39000 107.93000 112.46000 113.55000 108.80000
## 2019 108.10000 106.41000 113.02000 109.95000 114.95000 114.86000 111.24000
## 2020 109.49000 109.27000 104.04000 87.36000 89.33000 96.05000 96.95000
## 2021 106.84000 107.04000 114.51000 109.72000 115.43000 115.19000 112.16000
## 2022 109.25000 110.28000 118.85000 111.15000 120.33000 118.27000 113.36000
## 2023 36.21022 31.15808 24.67393
## Aug Sep Oct Nov Dec
## 2009 86.72000 87.57000 85.27000 91.86000 99.64000
## 2010 90.25000 89.00000 88.74000 93.13000 100.74000
## 2011 94.22000 92.33000 89.06000 96.86000 103.91000
## 2012 98.21000 93.94000 93.49000 99.61000 105.05000
## 2013 98.96000 97.74000 96.22000 101.24000 108.37000
## 2014 98.60000 98.25000 96.43000 100.64000 107.19000
## 2015 101.06000 100.64000 100.44000 104.90000 109.86000
## 2016 106.24000 104.83000 102.04000 106.50000 114.98000
## 2017 107.88000 106.21000 103.28000 110.39000 117.56000
## 2018 111.94000 107.54000 105.81000 112.16000 120.03000
## 2019 113.28000 111.66000 108.32000 116.10000 122.08000
## 2020 103.34000 106.72000 106.12000 110.70000 119.86000
## 2021 114.23000 113.82000 109.73000 116.70000 123.69000
## 2022 116.30000 0.00000 14.17444 60.38560 10.45998
## 2023
4. Descomposición de la serie temporal
library(stats)
fit<-stl(ivae_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)
## Jan Feb Mar Apr May Jun Jul
## 2009 86.15377 86.49919 86.84461 87.18449 87.52437 87.84656 88.16876
## 2010 89.50483 89.55197 89.59911 89.70565 89.81219 90.07589 90.33959
## 2011 92.54788 92.80018 93.05248 93.23603 93.41958 93.70270 93.98582
## 2012 95.72802 95.95512 96.18222 96.38290 96.58358 96.79664 97.00970
## 2013 97.47521 97.57322 97.67123 97.84762 98.02401 98.35200 98.68000
## 2014 100.08150 100.08549 100.08948 100.03009 99.97071 99.98445 99.99819
## 2015 100.72128 100.89900 101.07672 101.32014 101.56356 101.84109 102.11862
## 2016 103.41589 103.67540 103.93491 104.20808 104.48125 104.81383 105.14640
## 2017 106.09328 106.19299 106.29270 106.47626 106.65982 106.98854 107.31727
## 2018 108.63839 108.78771 108.93704 109.11286 109.28868 109.57224 109.85579
## 2019 111.30740 111.44661 111.58582 111.79265 111.99949 112.24804 112.49659
## 2020 106.67381 105.30788 103.94194 103.22310 102.50425 102.44703 102.38982
## 2021 110.10892 110.88032 111.65171 112.09770 112.54369 112.93383 113.32397
## 2022 114.67574 112.20774 109.73974 103.80647 97.87319 89.56160 81.25000
## 2023 28.93419 19.85969 10.78520
## Aug Sep Oct Nov Dec
## 2009 88.47482 88.78089 89.05729 89.33369 89.41926
## 2010 90.67349 91.00740 91.40828 91.80916 92.17852
## 2011 94.28058 94.57534 94.87627 95.17721 95.45261
## 2012 97.11123 97.21276 97.29100 97.36924 97.42223
## 2013 99.01091 99.34182 99.59470 99.84758 99.96454
## 2014 100.06211 100.12603 100.27021 100.41439 100.56784
## 2015 102.30013 102.48163 102.69463 102.90763 103.16176
## 2016 105.38312 105.61984 105.76006 105.90028 105.99678
## 2017 107.60717 107.89706 108.11577 108.33447 108.48643
## 2018 110.16682 110.47785 110.73752 110.99718 111.15229
## 2019 112.34925 112.20190 111.10444 110.00699 108.34040
## 2020 103.19377 103.99772 105.60660 107.21548 108.66220
## 2021 113.64767 113.97136 114.27344 114.57551 114.62563
## 2022 72.89467 64.53934 55.79897 47.05859 37.99639
## 2023
5. Cálculo de las tasas
library(dplyr)
library(zoo)
TC %>% 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) %>%
#Aquí se realiza el centrado
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 103.80647 -5.406678 -7.396437 5.4318940 -51.17066 NA
## May 2022 97.87319 -5.715708 -13.035383 3.4753263 -58.92788 NA
## Jun 2022 89.56160 -8.492207 -20.695513 0.8453435 -66.85175 NA
## Jul 2022 81.25000 -9.280311 -28.302899 -2.4392195 -74.76869 NA
## Aug 2022 72.89467 -10.283483 -35.859069 -6.2914208 -82.30096 NA
## Sep 2022 64.53934 -11.462196 -43.372318 -10.7020667 -90.17202 NA
## Oct 2022 55.79897 -13.542708 -51.170659 -15.6385664 NA NA
## Nov 2022 47.05859 -15.664043 -58.927879 -21.1051321 NA NA
## Dec 2022 37.99639 -19.257269 -66.851747 -27.1092694 NA NA
## Jan 2023 28.93419 -23.850158 -74.768689 -33.6624108 NA NA
## Feb 2023 19.85969 -31.362541 -82.300958 -40.5147965 NA NA
## Mar 2023 10.78520 -45.693039 -90.172023 -47.7145646 NA NA
6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura1 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura1 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()
COSTA RICA
1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/Usuario/Desktop/REBE/ivae_ts/ivae_ts.xlsx",
skip = 4)
ivae_ts$`Costa Rica` %>% ts(start = c(2009,1),
frequency = 12)->ivae
print(ivae)
## 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 106.31 108.14 117.49 105.61 108.90 109.10 110.06 110.34 110.48
## 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,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2009-2022 (septiembre)")+theme_bw()
2. proyección a Seis meses
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)
## Series: ivae
## ARIMA(1,0,0)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 sma1 drift
## 0.8511 -0.5096 0.2326
## s.e. 0.0419 0.0813 0.0423
##
## sigma^2 = 3.381: log likelihood = -311.14
## AIC=630.28 AICc=630.55 BIC=642.4
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.01327525 1.753237 1.164367 -0.006606559 1.216715 0.28915
## ACF1
## Training set -0.068264
pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("Años")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "IVAE")+autolayer(pronosticos$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
3. Serie ampliada
ivae_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2009,1),frequency = 12)
print(ivae_h)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 73.0500 70.5000 75.6800 70.1300 72.3000 73.4300 72.9300 72.0000
## 2010 75.1000 73.5300 79.9200 73.2700 75.7400 76.4300 76.1300 75.5800
## 2011 78.2700 76.7700 82.0000 76.0300 79.2300 79.6300 77.9900 77.8900
## 2012 82.3700 82.9500 86.0300 78.5500 82.2300 81.8300 80.6000 81.7700
## 2013 83.1000 82.7900 85.6200 81.1300 84.1200 83.7700 83.8800 83.9700
## 2014 86.4100 87.0400 89.1200 83.1200 86.0400 85.3600 86.6300 86.1700
## 2015 88.3000 90.0400 92.8600 88.5000 92.0900 92.5300 93.8400 92.7500
## 2016 94.5300 95.6000 96.3600 93.1300 95.3900 95.6600 94.9400 94.8400
## 2017 96.7100 96.9600 100.8500 94.8400 99.0600 99.9000 96.2600 96.6400
## 2018 99.2100 99.0000 103.5500 99.6200 104.5900 103.4300 101.4600 101.1000
## 2019 101.4800 101.9300 105.9400 99.9800 103.7800 103.6300 102.4500 101.4300
## 2020 102.2000 104.2300 102.6000 89.6500 91.8100 95.7800 91.8600 92.5100
## 2021 96.6300 100.2900 108.0900 101.6600 104.5000 104.7300 107.7700 105.7100
## 2022 106.3100 108.1400 117.4900 105.6100 108.9000 109.1000 110.0600 110.3400
## 2023 108.5389 110.5827 117.3577
## Sep Oct Nov Dec
## 2009 73.6400 76.7700 78.1800 78.3500
## 2010 77.1400 79.7400 82.1600 81.0600
## 2011 80.0500 83.5700 85.9300 84.6700
## 2012 82.7500 85.6900 89.2600 88.6300
## 2013 86.0400 88.5300 90.7700 90.8000
## 2014 88.1400 92.5500 94.0000 95.2300
## 2015 93.7800 96.6700 98.4300 97.8700
## 2016 98.1200 101.2600 103.9000 103.7900
## 2017 98.9900 103.9600 107.7100 108.1100
## 2018 101.6200 106.0900 108.9000 108.0100
## 2019 103.5700 109.0500 111.4700 111.0900
## 2020 97.3900 101.7200 105.1200 110.6100
## 2021 108.6200 111.2300 116.9100 119.8400
## 2022 110.4800 113.9698 118.1828 120.7984
## 2023
4. Descomposición de la serie temporal
library(stats)
fit<-stl(ivae_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)
## 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
5. Cálculo de las tasas
library(dplyr)
library(zoo)
TC %>% 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) %>%
#Aquí se realiza el centrado
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 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
6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura2 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura2 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()
GUATEMALA
1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/Usuario/Desktop/REBE/ivae_ts/ivae_ts.xlsx",
skip = 4)
ivae_ts$Guatemala %>% ts(start = c(2009,1),
frequency = 12)->ivae
print(ivae)
## 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 134.95 134.14 139.23 135.83 135.53 132.00 135.09 136.00 133.97
## 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,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2009-2022 (septiembre)")+theme_bw()
2. proyección a Seis meses
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)
## Series: ivae
## ARIMA(1,0,1)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 ma1 sma1 drift
## 0.7685 0.2206 -0.7846 0.3047
## s.e. 0.0640 0.1040 0.0732 0.0147
##
## sigma^2 = 1.948: log likelihood = -272.4
## AIC=554.8 AICc=555.2 BIC=569.95
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.02030934 1.326456 0.8825237 -0.03497516 0.8008833 0.2039824
## ACF1
## Training set 0.003115459
pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("Años")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "IVAE")+autolayer(pronosticos$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
3. Serie ampliada
ivae_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2009,1),frequency = 12)
print(ivae_h)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 86.6500 84.9500 90.1700 87.7900 85.6900 83.9200 87.0900 85.9000
## 2010 88.4300 87.0900 94.1400 89.6800 88.2800 87.4900 88.0300 87.3500
## 2011 92.1600 91.2800 96.9600 93.6000 92.2000 91.6000 92.6500 92.6100
## 2012 95.0500 94.9500 101.1000 95.1300 95.5800 94.1300 94.9700 95.3100
## 2013 99.0700 98.8100 101.7200 101.2000 99.5000 96.7200 98.6400 98.6700
## 2014 102.7500 102.5700 106.7600 104.8000 104.4000 101.0500 103.7800 102.2000
## 2015 107.7600 107.1500 111.7400 107.6600 106.6700 105.6300 108.7200 107.5300
## 2016 109.7400 109.4400 112.9600 112.2900 111.1200 108.4000 109.3500 110.4100
## 2017 115.4200 114.3000 118.0700 114.7000 113.7200 111.6300 113.8200 113.9300
## 2018 117.7500 117.7700 121.7700 119.5900 118.7100 116.3500 118.2200 118.0400
## 2019 122.0800 122.7600 126.0500 123.9500 123.6700 120.4500 122.9300 121.9400
## 2020 127.0100 125.5100 121.3800 112.7300 111.4900 111.5500 118.5000 120.6000
## 2021 128.8800 128.6100 133.2900 130.0600 130.0100 127.5300 131.2200 130.1300
## 2022 134.9500 134.1400 139.2300 135.8300 135.5300 132.0000 135.0900 136.0000
## 2023 137.9819 137.2163 140.3554
## Sep Oct Nov Dec
## 2009 84.6500 87.0600 87.9400 95.1900
## 2010 86.9200 88.6900 91.3500 98.9200
## 2011 92.0800 91.7800 95.8600 101.4300
## 2012 94.0200 96.3200 98.9200 104.1100
## 2013 97.7200 99.4800 102.1600 106.3000
## 2014 101.7800 103.9000 107.0900 112.2700
## 2015 106.6400 108.4500 111.4400 115.2400
## 2016 109.8000 110.4300 114.9900 120.6300
## 2017 112.0700 113.6800 116.9100 122.5600
## 2018 115.4200 117.9800 121.0400 125.2000
## 2019 120.7800 122.9900 126.9400 130.4500
## 2020 121.7300 125.2000 128.0500 135.0400
## 2021 128.7700 130.6200 135.3400 140.7700
## 2022 133.9700 135.3104 138.3894 143.2999
## 2023
4. Descomposición de la serie temporal
library(stats)
fit<-stl(ivae_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)
## 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
5. Cálculo de las tasas
library(dplyr)
library(zoo)
TC %>% 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) %>%
#Aquí se realiza el centrado
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
6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura3 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura3 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()
HONDURAS
1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/Usuario/Desktop/REBE/ivae_ts/ivae_ts.xlsx",
skip = 4)
ivae_ts$Honduras %>% ts(start = c(2009,1),
frequency = 12)->ivae
print(ivae)
## 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 0.00
## 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,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2009-2022 (agosto)")+theme_bw()
2. proyección a Seis meses
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)
## Series: ivae
## ARIMA(1,1,0)
##
## Coefficients:
## ar1
## -0.7385
## s.e. 0.1367
##
## sigma^2 = 513: log likelihood = -744.3
## AIC=1492.6 AICc=1492.67 BIC=1498.8
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -0.4420486 22.51169 10.03622 -Inf Inf 0.782071 -0.03479806
pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("Años")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "IVAE")+autolayer(pronosticos$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
3. Serie ampliada
ivae_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2009,1),frequency = 12)
print(ivae_h)
## Jan Feb Mar Apr May Jun Jul
## 2009 157.26000 159.33000 169.91000 156.18000 164.17000 163.04000 155.42000
## 2010 165.28000 166.91000 179.91000 165.46000 173.89000 171.00000 162.53000
## 2011 176.96000 179.46000 190.71000 175.18000 184.30000 182.33000 175.83000
## 2012 181.51000 189.25000 202.52000 183.81000 193.45000 192.28000 185.89000
## 2013 189.68000 192.66000 196.37000 195.49000 199.00000 194.38000 190.45000
## 2014 194.20000 197.58000 205.41000 197.36000 207.03000 198.09000 194.18000
## 2015 200.82000 202.02000 214.06000 206.39000 206.66000 206.13000 201.94000
## 2016 207.87000 210.56000 220.51000 211.07000 214.45000 216.00000 205.61000
## 2017 219.37000 221.50000 233.93000 218.03000 225.53000 225.90000 216.75000
## 2018 228.97000 228.12000 237.11000 227.12000 234.88000 234.03000 225.04000
## 2019 235.30000 235.08000 246.40000 234.80000 241.51000 235.46000 238.02000
## 2020 242.49000 241.65000 218.27000 186.88000 189.07000 208.71000 209.30000
## 2021 229.97000 236.28000 251.05000 235.96000 242.36000 247.40000 239.81000
## 2022 247.27000 246.62000 263.70000 248.77000 254.73000 256.23000 246.20000
## 2023 81.21770 140.99605 96.84976
## Aug Sep Oct Nov Dec
## 2009 159.89000 157.82000 166.33000 163.97000 176.16000
## 2010 166.65000 175.18000 172.00000 175.48000 186.89000
## 2011 185.67000 182.03000 185.82000 188.18000 198.66000
## 2012 193.61000 188.79000 199.97000 199.48000 203.10000
## 2013 196.66000 191.32000 201.79000 201.54000 213.57000
## 2014 199.21000 197.73000 205.50000 203.26000 221.72000
## 2015 207.78000 204.91000 213.81000 214.73000 231.40000
## 2016 215.98000 212.31000 220.76000 227.59000 245.58000
## 2017 229.08000 226.26000 232.75000 235.80000 251.23000
## 2018 238.66000 232.55000 244.93000 245.16000 262.48000
## 2019 244.65000 239.69000 252.72000 250.26000 273.80000
## 2020 225.80000 230.24000 249.34000 218.89000 258.08000
## 2021 256.77000 246.87000 265.45000 264.73000 279.05000
## 2022 272.14000 0.00000 200.97530 52.55511 162.16337
## 2023
4. Descomposición de la serie temporal
library(stats)
fit<-stl(ivae_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)
## Jan Feb Mar Apr May Jun Jul
## 2009 161.00065 161.34138 161.68210 162.06558 162.44906 162.88074 163.31242
## 2010 167.18865 168.02611 168.86356 169.69597 170.52838 171.38679 172.24521
## 2011 178.04945 179.16650 180.28355 181.28755 182.29155 183.13649 183.98144
## 2012 188.59749 189.37442 190.15135 190.99547 191.83958 192.52434 193.20909
## 2013 194.97515 195.19501 195.41486 195.72971 196.04456 196.58070 197.11684
## 2014 199.74100 200.06336 200.38572 200.71215 201.03858 201.52115 202.00371
## 2015 205.25265 205.86759 206.48253 207.16431 207.84608 208.62798 209.40988
## 2016 213.17997 213.68349 214.18701 214.84549 215.50396 216.51401 217.52406
## 2017 223.53333 224.37948 225.22563 225.97995 226.73426 227.50131 228.26836
## 2018 232.32311 232.88403 233.44495 234.14017 234.83539 235.75829 236.68120
## 2019 240.60499 240.99211 241.37924 241.88789 242.39654 243.17248 243.94841
## 2020 232.20863 229.49726 226.78588 224.96836 223.15084 222.50000 221.84916
## 2021 237.34711 239.45260 241.55810 243.68401 245.80993 247.97586 250.14180
## 2022 255.71697 251.44948 247.18200 236.86693 226.55186 212.45725 198.36263
## 2023 109.85294 94.48044 79.10794
## Aug Sep Oct Nov Dec
## 2009 163.77371 164.23500 164.90296 165.57091 166.37978
## 2010 173.08428 173.92336 174.90898 175.89461 176.97203
## 2011 184.68272 185.38399 186.19094 186.99789 187.79769
## 2012 193.50661 193.80412 194.08853 194.37294 194.67404
## 2013 197.58785 198.05886 198.50932 198.95979 199.35039
## 2014 202.50666 203.00962 203.55823 204.10683 204.67974
## 2015 210.07334 210.73680 211.36660 211.99641 212.58819
## 2016 218.60409 219.68413 220.69045 221.69678 222.61505
## 2017 228.98217 229.69598 230.39028 231.08459 231.70385
## 2018 237.52224 238.36329 239.02384 239.68438 240.14469
## 2019 243.77102 243.59363 241.25311 238.91258 235.56061
## 2020 223.32194 224.79471 228.08486 231.37501 234.36106
## 2021 251.62905 253.11629 254.10586 255.09543 255.40620
## 2022 184.24495 170.12727 155.33352 140.53977 125.19636
## 2023
5. Cálculo de las tasas
library(dplyr)
library(zoo)
TC %>% 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) %>%
#Aquí se realiza el centrado
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 236.86693 -4.173066 -2.797510 8.407653 -38.87055 NA
## May 2022 226.55186 -4.354794 -7.834534 6.839290 -44.90698 NA
## Jun 2022 212.45725 -6.221365 -14.323418 4.614274 -50.98147 NA
## Jul 2022 198.36263 -6.634097 -20.699929 1.756832 -57.04120 NA
## Aug 2022 184.24495 -7.117107 -26.779142 -1.587444 -62.42568 NA
## Sep 2022 170.12727 -7.662452 -32.786916 -5.404267 -67.99607 NA
## Oct 2022 155.33352 -8.695694 -38.870548 -9.614631 NA NA
## Nov 2022 140.53977 -9.523860 -44.906981 -14.217972 NA NA
## Dec 2022 125.19636 -10.917490 -50.981473 -19.201055 NA NA
## Jan 2023 109.85294 -12.255481 -57.041200 -24.569232 NA NA
## Feb 2023 94.48044 -13.993707 -62.425676 -30.092704 NA NA
## Mar 2023 79.10794 -16.270562 -67.996074 -35.804683 NA NA
6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura4 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura4 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()
NICARAGUA
1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/Usuario/Desktop/REBE/ivae_ts/ivae_ts.xlsx",
skip = 4)
ivae_ts$Nicaragua %>% ts(start = c(2009,1),
frequency = 12)->ivae
print(ivae)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2009 105.02 99.61 99.82 97.86 105.33 102.92 111.29 106.11 100.80 103.08
## 2010 107.22 102.04 106.17 100.25 108.47 107.98 116.44 110.70 106.35 110.07
## 2011 112.84 105.40 114.89 106.19 118.58 116.46 126.36 118.61 112.82 113.74
## 2012 128.39 116.85 118.64 112.51 126.31 118.10 130.29 123.88 117.08 126.19
## 2013 132.07 122.40 122.30 126.76 132.79 123.18 138.36 130.19 125.12 130.05
## 2014 135.67 129.80 132.03 128.86 139.04 130.03 143.73 133.05 131.24 137.49
## 2015 141.73 135.06 139.10 131.32 143.71 134.69 151.29 141.67 141.01 146.60
## 2016 148.01 141.73 143.00 140.87 153.13 144.24 155.80 149.66 143.57 149.07
## 2017 159.90 150.21 154.66 144.21 159.98 150.52 161.86 154.39 147.57 154.82
## 2018 165.61 154.20 158.41 150.62 151.56 130.54 153.23 148.98 141.06 143.07
## 2019 151.81 138.11 139.71 137.92 145.19 135.01 150.32 143.56 138.82 147.62
## 2020 153.26 145.11 140.70 124.93 134.77 130.15 148.61 139.39 140.77 148.10
## 2021 155.55 148.15 152.24 145.97 159.52 155.10 165.86 154.92 151.38 160.56
## 2022 166.46 154.54 161.05 153.45 167.00 159.88 171.47 162.14 154.97
## Nov Dec
## 2009 109.25 120.21
## 2010 116.56 124.67
## 2011 125.90 128.32
## 2012 130.71 142.11
## 2013 134.02 147.29
## 2014 141.38 157.08
## 2015 148.63 163.14
## 2016 155.85 171.41
## 2017 164.86 176.56
## 2018 153.82 165.28
## 2019 154.01 165.59
## 2020 145.86 164.76
## 2021 165.89 178.74
## 2022
autoplot(ivae,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2009-2022 (septiembre)")+theme_bw()
2. proyección a Seis meses
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)
## Series: ivae
## ARIMA(0,1,1)(0,1,1)[12]
##
## Coefficients:
## ma1 sma1
## -0.3487 -0.7878
## s.e. 0.0851 0.0853
##
## sigma^2 = 13.88: log likelihood = -420.46
## AIC=846.91 AICc=847.07 BIC=855.98
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.0336619 3.552316 2.514308 -0.06554888 1.814601 0.3485734
## ACF1
## Training set 0.02696206
pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("Años")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "IVAE")+autolayer(pronosticos$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
3. Serie ampliada
ivae_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2009,1),frequency = 12)
print(ivae_h)
## 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
4. Descomposición de la serie temporal
library(stats)
fit<-stl(ivae_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)
## 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
5. Cálculo de las tasas
library(dplyr)
library(zoo)
TC %>% 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) %>%
#Aquí se realiza el centrado
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
6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura5 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura5 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()
PANAMA
1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/Usuario/Desktop/REBE/ivae_ts/ivae_ts.xlsx",
skip = 4)
ivae_ts$Panamá %>% ts(start = c(2009,1),
frequency = 12)->ivae
print(ivae)
## 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 0.00
## 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,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2009-2022 (agosto)")+theme_bw()
2. proyección a Seis meses
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)
## Series: ivae
## ARIMA(1,1,0)(0,0,2)[12]
##
## Coefficients:
## ar1 sma1 sma2
## -0.6400 0.4505 0.2119
## s.e. 0.1594 0.1449 0.1382
##
## sigma^2 = 920.4: log likelihood = -792.53
## AIC=1593.07 AICc=1593.32 BIC=1605.47
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -1.020251 29.96787 10.37836 -Inf Inf 0.4442381 0.02343955
pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("Años")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "IVAE")+autolayer(pronosticos$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
3. Serie ampliada
ivae_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2009,1),frequency = 12)
print(ivae_h)
## Jan Feb Mar Apr May Jun Jul
## 2009 195.71000 189.71000 204.64000 188.06000 193.65000 199.60000 188.20000
## 2010 201.01000 200.42000 220.03000 203.11000 202.73000 210.75000 198.31000
## 2011 212.36000 218.50000 228.61000 218.93000 227.12000 226.92000 210.41000
## 2012 233.23000 237.88000 260.05000 237.89000 248.64000 251.02000 239.86000
## 2013 253.29000 254.04000 276.60000 262.60000 268.38000 269.16000 256.27000
## 2014 265.09000 267.31000 286.56000 275.53000 274.45000 283.35000 268.30000
## 2015 281.48000 276.75000 307.31000 280.85000 281.06000 294.76000 279.85000
## 2016 292.53000 289.67000 318.79000 292.13000 296.56000 306.77000 293.76000
## 2017 305.84000 307.27000 344.01000 309.60000 316.06000 324.68000 304.97000
## 2018 320.57000 323.91000 349.99000 311.69000 317.94000 324.94000 308.98000
## 2019 332.39000 332.73000 353.65000 319.17000 325.72000 332.45000 325.11000
## 2020 346.02000 341.78000 357.06000 243.21000 222.17000 233.12000 240.66000
## 2021 304.59000 322.77000 354.90000 307.26000 314.67000 309.91000 306.48000
## 2022 354.01000 368.38000 390.50000 334.96000 344.38000 348.03000 317.45000
## 2023 132.67108 178.29139 163.03503
## Aug Sep Oct Nov Dec
## 2009 190.96000 195.45000 204.89000 185.82000 190.56000
## 2010 206.22000 205.20000 213.91000 202.49000 205.63000
## 2011 225.69000 222.92000 233.74000 226.59000 231.09000
## 2012 246.47000 238.38000 249.62000 251.28000 247.71000
## 2013 265.08000 259.72000 280.51000 272.24000 270.52000
## 2014 278.43000 272.53000 296.66000 282.62000 292.03000
## 2015 290.40000 283.40000 310.57000 295.47000 300.89000
## 2016 303.34000 296.96000 322.82000 309.18000 312.22000
## 2017 318.19000 310.13000 335.94000 322.13000 324.80000
## 2018 323.54000 315.15000 333.20000 328.79000 330.41000
## 2019 336.06000 332.01000 346.53000 341.09000 341.27000
## 2020 242.56000 259.86000 298.72000 296.58000 339.78000
## 2021 318.85000 317.91000 344.08000 332.46000 395.90000
## 2022 359.51000 0.00000 241.30199 89.29547 212.48816
## 2023
4. Descomposición de la serie temporal
library(stats)
fit<-stl(ivae_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)
## Jan Feb Mar Apr May Jun Jul Aug
## 2009 189.9469 190.8241 191.7012 192.4636 193.2259 193.9240 194.6220 195.3927
## 2010 199.3663 200.4517 201.5370 202.8639 204.1908 205.5647 206.9387 207.9025
## 2011 213.5534 215.1564 216.7595 218.7247 220.6899 222.8085 224.9272 226.6780
## 2012 235.5871 237.5231 239.4591 241.2927 243.1262 244.8587 246.5913 247.9079
## 2013 254.9013 256.7547 258.6081 260.7649 262.9217 264.8138 266.7059 267.7179
## 2014 271.3855 272.4069 273.4284 274.8318 276.2352 277.9335 279.6318 280.8764
## 2015 284.6235 285.3962 286.1690 287.2949 288.4209 289.7912 291.1615 292.2715
## 2016 296.6685 297.5763 298.4842 299.7261 300.9681 302.5083 304.0485 305.5616
## 2017 312.2674 313.3557 314.4440 315.7083 316.9725 318.3522 319.7318 320.6623
## 2018 322.3633 322.4438 322.5243 322.8393 323.1544 323.8823 324.6103 325.3733
## 2019 328.7818 329.6982 330.6146 331.7697 332.9247 334.1980 335.4714 335.7124
## 2020 313.3501 304.9473 296.5444 290.3711 284.1978 281.2928 278.3878 279.4386
## 2021 303.9728 308.8221 313.6714 317.2110 320.7506 324.4097 328.0687 332.0761
## 2022 347.6663 342.8936 338.1209 324.1536 310.1863 290.6329 271.0796 251.6158
## 2023 149.9401 129.3140 108.6878
## Sep Oct Nov Dec
## 2009 196.1634 197.0111 197.8587 198.6125
## 2010 208.8663 209.9586 211.0510 212.3022
## 2011 228.4289 230.1667 231.9046 233.7458
## 2012 249.2246 250.5615 251.8983 253.3998
## 2013 268.7299 269.3483 269.9666 270.6760
## 2014 282.1211 282.8161 283.5111 284.0673
## 2015 293.3816 294.2331 295.0846 295.8765
## 2016 307.0746 308.4756 309.8767 311.0720
## 2017 321.5927 321.9245 322.2563 322.3098
## 2018 326.1364 326.7918 327.4472 328.1145
## 2019 335.9534 332.2721 328.5908 320.9705
## 2020 280.4895 285.7369 290.9842 297.4785
## 2021 336.0836 339.6585 343.2334 345.4499
## 2022 232.1519 211.8570 191.5621 170.7511
## 2023
5. Cálculo de las tasas
library(dplyr)
library(zoo)
TC %>% 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) %>%
#Aquí se realiza el centrado
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 324.1536 -4.130864 2.188634 14.2227881 -37.62646 NA
## May 2022 310.1863 -4.308857 -3.293628 12.7525107 -44.18896 NA
## Jun 2022 290.6329 -6.303737 -10.411751 10.4647017 -50.57137 NA
## Jul 2022 271.0796 -6.727843 -17.371092 7.4006645 -56.87240 NA
## Aug 2022 251.6158 -7.180125 -24.229501 3.7018096 -62.28744 NA
## Sep 2022 232.1519 -7.735546 -30.924352 -0.5964696 -67.85535 NA
## Oct 2022 211.8570 -8.742075 -37.626461 -5.3536831 NA NA
## Nov 2022 191.5621 -9.579524 -44.188960 -10.5567998 NA NA
## Dec 2022 170.7511 -10.863831 -50.571371 -16.1173208 NA NA
## Jan 2023 149.9401 -12.187905 -56.872401 -22.0402815 NA NA
## Feb 2023 129.3140 -13.756281 -62.287441 -28.0571030 NA NA
## Mar 2023 108.6878 -15.950473 -67.855352 -34.2086479 NA NA
6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura6 %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2009,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura6 %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()
TODOS LOS PAISES
1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/Usuario/Desktop/REBE/ivae_ts/ivae_ts.xlsx",
skip = 4)
ivae_ts %>% ts(start = c(2009,1),
frequency = 12)->ivae
print(ivae)
## Costa Rica El Salvador Guatemala Honduras Nicaragua Panamá
## 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
## Nov 2009 78.18 91.86 87.94 163.97 109.25 185.82
## Dec 2009 78.35 99.64 95.19 176.16 120.21 190.56
## Jan 2010 75.10 85.56 88.43 165.28 107.22 201.01
## Feb 2010 73.53 84.69 87.09 166.91 102.04 200.42
## Mar 2010 79.92 90.90 94.14 179.91 106.17 220.03
## Apr 2010 73.27 85.94 89.68 165.46 100.25 203.11
## May 2010 75.74 94.33 88.28 173.89 108.47 202.73
## Jun 2010 76.43 92.23 87.49 171.00 107.98 210.75
## Jul 2010 76.13 87.18 88.03 162.53 116.44 198.31
## Aug 2010 75.58 90.25 87.35 166.65 110.70 206.22
## Sep 2010 77.14 89.00 86.92 175.18 106.35 205.20
## Oct 2010 79.74 88.74 88.69 172.00 110.07 213.91
## Nov 2010 82.16 93.13 91.35 175.48 116.56 202.49
## Dec 2010 81.06 100.74 98.92 186.89 124.67 205.63
## Jan 2011 78.27 90.27 92.16 176.96 112.84 212.36
## Feb 2011 76.77 86.73 91.28 179.46 105.40 218.50
## Mar 2011 82.00 94.32 96.96 190.71 114.89 228.61
## Apr 2011 76.03 90.79 93.60 175.18 106.19 218.93
## May 2011 79.23 98.50 92.20 184.30 118.58 227.12
## Jun 2011 79.63 97.59 91.60 182.33 116.46 226.92
## Jul 2011 77.99 92.16 92.65 175.83 126.36 210.41
## Aug 2011 77.89 94.22 92.61 185.67 118.61 225.69
## Sep 2011 80.05 92.33 92.08 182.03 112.82 222.92
## Oct 2011 83.57 89.06 91.78 185.82 113.74 233.74
## Nov 2011 85.93 96.86 95.86 188.18 125.90 226.59
## Dec 2011 84.67 103.91 101.43 198.66 128.32 231.09
## Jan 2012 82.37 92.65 95.05 181.51 128.39 233.23
## Feb 2012 82.95 91.20 94.95 189.25 116.85 237.88
## Mar 2012 86.03 98.46 101.10 202.52 118.64 260.05
## Apr 2012 78.55 91.23 95.13 183.81 112.51 237.89
## May 2012 82.23 102.83 95.58 193.45 126.31 248.64
## Jun 2012 81.83 102.84 94.13 192.28 118.10 251.02
## Jul 2012 80.60 93.61 94.97 185.89 130.29 239.86
## Aug 2012 81.77 98.21 95.31 193.61 123.88 246.47
## Sep 2012 82.75 93.94 94.02 188.79 117.08 238.38
## Oct 2012 85.69 93.49 96.32 199.97 126.19 249.62
## Nov 2012 89.26 99.61 98.92 199.48 130.71 251.28
## Dec 2012 88.63 105.05 104.11 203.10 142.11 247.71
## Jan 2013 83.10 95.67 99.07 189.68 132.07 253.29
## Feb 2013 82.79 90.77 98.81 192.66 122.40 254.04
## Mar 2013 85.62 96.12 101.72 196.37 122.30 276.60
## Apr 2013 81.13 96.34 101.20 195.49 126.76 262.60
## May 2013 84.12 103.08 99.50 199.00 132.79 268.38
## Jun 2013 83.77 101.58 96.72 194.38 123.18 269.16
## Jul 2013 83.88 96.42 98.64 190.45 138.36 256.27
## Aug 2013 83.97 98.96 98.67 196.66 130.19 265.08
## Sep 2013 86.04 97.74 97.72 191.32 125.12 259.72
## Oct 2013 88.53 96.22 99.48 201.79 130.05 280.51
## Nov 2013 90.77 101.24 102.16 201.54 134.02 272.24
## Dec 2013 90.80 108.37 106.30 213.57 147.29 270.52
## Jan 2014 86.41 98.70 102.75 194.20 135.67 265.09
## Feb 2014 87.04 94.70 102.57 197.58 129.80 267.31
## Mar 2014 89.12 101.30 106.76 205.41 132.03 286.56
## Apr 2014 83.12 97.12 104.80 197.36 128.86 275.53
## May 2014 86.04 103.86 104.40 207.03 139.04 274.45
## Jun 2014 85.36 104.73 101.05 198.09 130.03 283.35
## Jul 2014 86.63 98.48 103.78 194.18 143.73 268.30
## Aug 2014 86.17 98.60 102.20 199.21 133.05 278.43
## Sep 2014 88.14 98.25 101.78 197.73 131.24 272.53
## Oct 2014 92.55 96.43 103.90 205.50 137.49 296.66
## Nov 2014 94.00 100.64 107.09 203.26 141.38 282.62
## Dec 2014 95.23 107.19 112.27 221.72 157.08 292.03
## Jan 2015 88.30 98.87 107.76 200.82 141.73 281.48
## Feb 2015 90.04 94.82 107.15 202.02 135.06 276.75
## Mar 2015 92.86 103.15 111.74 214.06 139.10 307.31
## Apr 2015 88.50 98.75 107.66 206.39 131.32 280.85
## May 2015 92.09 105.65 106.67 206.66 143.71 281.06
## Jun 2015 92.53 105.45 105.63 206.13 134.69 294.76
## Jul 2015 93.84 101.67 108.72 201.94 151.29 279.85
## Aug 2015 92.75 101.06 107.53 207.78 141.67 290.40
## Sep 2015 93.78 100.64 106.64 204.91 141.01 283.40
## Oct 2015 96.67 100.44 108.45 213.81 146.60 310.57
## Nov 2015 98.43 104.90 111.44 214.73 148.63 295.47
## Dec 2015 97.87 109.86 115.24 231.40 163.14 300.89
## Jan 2016 94.53 99.25 109.74 207.87 148.01 292.53
## Feb 2016 95.60 97.76 109.44 210.56 141.73 289.67
## Mar 2016 96.36 102.58 112.96 220.51 143.00 318.79
## Apr 2016 93.13 103.43 112.29 211.07 140.87 292.13
## May 2016 95.39 107.76 111.12 214.45 153.13 296.56
## Jun 2016 95.66 110.71 108.40 216.00 144.24 306.77
## Jul 2016 94.94 104.01 109.35 205.61 155.80 293.76
## Aug 2016 94.84 106.24 110.41 215.98 149.66 303.34
## Sep 2016 98.12 104.83 109.80 212.31 143.57 296.96
## Oct 2016 101.26 102.04 110.43 220.76 149.07 322.82
## Nov 2016 103.90 106.50 114.99 227.59 155.85 309.18
## Dec 2016 103.79 114.98 120.63 245.58 171.41 312.22
## Jan 2017 96.71 101.41 115.42 219.37 159.90 305.84
## Feb 2017 96.96 98.97 114.30 221.50 150.21 307.27
## Mar 2017 100.85 108.44 118.07 233.93 154.66 344.01
## Apr 2017 94.84 101.40 114.70 218.03 144.21 309.60
## May 2017 99.06 110.85 113.72 225.53 159.98 316.06
## Jun 2017 99.90 113.63 111.63 225.90 150.52 324.68
## Jul 2017 96.26 105.51 113.82 216.75 161.86 304.97
## Aug 2017 96.64 107.88 113.93 229.08 154.39 318.19
## Sep 2017 98.99 106.21 112.07 226.26 147.57 310.13
## Oct 2017 103.96 103.28 113.68 232.75 154.82 335.94
## Nov 2017 107.71 110.39 116.91 235.80 164.86 322.13
## Dec 2017 108.11 117.56 122.56 251.23 176.56 324.80
## Jan 2018 99.21 105.17 117.75 228.97 165.61 320.57
## Feb 2018 99.00 102.53 117.77 228.12 154.20 323.91
## Mar 2018 103.55 108.39 121.77 237.11 158.41 349.99
## Apr 2018 99.62 107.93 119.59 227.12 150.62 311.69
## May 2018 104.59 112.46 118.71 234.88 151.56 317.94
## Jun 2018 103.43 113.55 116.35 234.03 130.54 324.94
## Jul 2018 101.46 108.80 118.22 225.04 153.23 308.98
## Aug 2018 101.10 111.94 118.04 238.66 148.98 323.54
## Sep 2018 101.62 107.54 115.42 232.55 141.06 315.15
## Oct 2018 106.09 105.81 117.98 244.93 143.07 333.20
## Nov 2018 108.90 112.16 121.04 245.16 153.82 328.79
## Dec 2018 108.01 120.03 125.20 262.48 165.28 330.41
## Jan 2019 101.48 108.10 122.08 235.30 151.81 332.39
## Feb 2019 101.93 106.41 122.76 235.08 138.11 332.73
## Mar 2019 105.94 113.02 126.05 246.40 139.71 353.65
## Apr 2019 99.98 109.95 123.95 234.80 137.92 319.17
## May 2019 103.78 114.95 123.67 241.51 145.19 325.72
## Jun 2019 103.63 114.86 120.45 235.46 135.01 332.45
## Jul 2019 102.45 111.24 122.93 238.02 150.32 325.11
## Aug 2019 101.43 113.28 121.94 244.65 143.56 336.06
## Sep 2019 103.57 111.66 120.78 239.69 138.82 332.01
## Oct 2019 109.05 108.32 122.99 252.72 147.62 346.53
## Nov 2019 111.47 116.10 126.94 250.26 154.01 341.09
## Dec 2019 111.09 122.08 130.45 273.80 165.59 341.27
## Jan 2020 102.20 109.49 127.01 242.49 153.26 346.02
## Feb 2020 104.23 109.27 125.51 241.65 145.11 341.78
## Mar 2020 102.60 104.04 121.38 218.27 140.70 357.06
## Apr 2020 89.65 87.36 112.73 186.88 124.93 243.21
## May 2020 91.81 89.33 111.49 189.07 134.77 222.17
## Jun 2020 95.78 96.05 111.55 208.71 130.15 233.12
## Jul 2020 91.86 96.95 118.50 209.30 148.61 240.66
## Aug 2020 92.51 103.34 120.60 225.80 139.39 242.56
## Sep 2020 97.39 106.72 121.73 230.24 140.77 259.86
## Oct 2020 101.72 106.12 125.20 249.34 148.10 298.72
## Nov 2020 105.12 110.70 128.05 218.89 145.86 296.58
## Dec 2020 110.61 119.86 135.04 258.08 164.76 339.78
## Jan 2021 96.63 106.84 128.88 229.97 155.55 304.59
## Feb 2021 100.29 107.04 128.61 236.28 148.15 322.77
## Mar 2021 108.09 114.51 133.29 251.05 152.24 354.90
## Apr 2021 101.66 109.72 130.06 235.96 145.97 307.26
## May 2021 104.50 115.43 130.01 242.36 159.52 314.67
## Jun 2021 104.73 115.19 127.53 247.40 155.10 309.91
## Jul 2021 107.77 112.16 131.22 239.81 165.86 306.48
## Aug 2021 105.71 114.23 130.13 256.77 154.92 318.85
## Sep 2021 108.62 113.82 128.77 246.87 151.38 317.91
## Oct 2021 111.23 109.73 130.62 265.45 160.56 344.08
## Nov 2021 116.91 116.70 135.34 264.73 165.89 332.46
## Dec 2021 119.84 123.69 140.77 279.05 178.74 395.90
## Jan 2022 106.31 109.25 134.95 247.27 166.46 354.01
## Feb 2022 108.14 110.28 134.14 246.62 154.54 368.38
## Mar 2022 117.49 118.85 139.23 263.70 161.05 390.50
## Apr 2022 105.61 111.15 135.83 248.77 153.45 334.96
## May 2022 108.90 120.33 135.53 254.73 167.00 344.38
## Jun 2022 109.10 118.27 132.00 256.23 159.88 348.03
## Jul 2022 110.06 113.36 135.09 246.20 171.47 317.45
## Aug 2022 110.34 116.30 136.00 272.14 162.14 359.51
## Sep 2022 110.48 0.00 133.97 0.00 154.97 0.00
autoplot(ivae,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2009-2022 (agosto - septiembre)")+theme_bw()
2.Función para visualizar las T1_T2 de Centroamerica y
Panama
unir1<-tabla_coyuntura1 %>% as.data.frame() %>% select(T_1_12)
unir2<-tabla_coyuntura2 %>% as.data.frame() %>% select(T_1_12)
unir3<-tabla_coyuntura3 %>% as.data.frame() %>% select(T_1_12)
unir4<-tabla_coyuntura4 %>% as.data.frame() %>% select(T_1_12)
unir5<-tabla_coyuntura5 %>% as.data.frame() %>% select(T_1_12)
unir6<-tabla_coyuntura6 %>% as.data.frame() %>% select(T_1_12)
final<- as.data.frame(c(unir1,unir2,unir3,unir4,unir5,unir6))
final %>% as.data.frame() %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()
