AnalisisCoyunturaEconomia
Sofia Lissette Mancía Chacón
2022-11-30
1. Importar Datos
library(ggplot2)
library(forecast)
library(readxl)
IVAE_CP <- read_excel("C:/Users/MINEDUCYT/Desktop/Analisis eco/IVAE_CP.xlsx",
col_types = c("skip", "numeric", "numeric","numeric","numeric","numeric","numeric"),
skip = 5)
IVAE_CP %>% ts(start = c(2009,1),frequency = 12)-> DATA_IVAE_CP
print(DATA_IVAE_CP)## IVAE_CR IVAE_ES IVAE_GU IVAE_HO IVAE_NI IVAE_PA
## 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 92.20 184.30 118.57 227.12
## 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 96.32 199.97 126.20 249.62
## 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 98.70 102.75 194.20 135.68 265.09
## 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 98.25 101.78 197.73 131.23 272.53
## 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 94.94 104.01 109.35 205.61 155.81 293.76
## 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 102.45 111.24 122.93 238.02 150.33 325.11
## 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 111.47 116.10 126.94 250.26 154.00 341.09
## 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 97.39 106.72 121.73 230.24 140.78 259.86
## 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 96.63 106.84 128.88 229.97 155.54 304.59
## 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 107.77 112.16 131.22 239.81 165.84 306.48
## Aug 2021 105.71 114.23 130.13 256.77 154.92 318.85
## Sep 2021 108.62 113.82 128.77 246.87 151.40 317.91
## Oct 2021 111.23 109.73 130.62 265.45 160.62 344.08
## Nov 2021 116.91 116.70 135.34 264.73 165.95 332.46
## Dec 2021 119.84 123.69 140.77 279.05 178.63 395.90
## Jan 2022 106.31 109.25 134.95 247.27 166.47 354.01
## Feb 2022 108.14 110.28 134.14 246.62 154.51 368.38
## Mar 2022 117.49 118.85 139.23 263.70 161.07 390.50
## Apr 2022 105.61 111.15 135.83 248.77 153.46 334.96
## May 2022 108.90 120.33 135.53 254.73 166.82 344.38
## Jun 2022 109.10 118.27 132.00 256.23 160.07 348.03
## Jul 2022 110.06 113.36 135.09 246.20 171.29 317.45
## Aug 2022 110.34 116.30 136.00 272.14 161.96 359.51
## Sep 2022 110.48 NA 133.97 NA NA NAautoplot(DATA_IVAE_CP,xlab = "años",ylab = "Indice",main = "IVAE total de los países de Centroamerica y Pánama, periodo 2009-2022 (agosto)") + autolayer(DATA_IVAE_CP)
#Ejemplo: Calcular IVAE Total de Costa Rica
1. Importar los datos
library(ggplot2)
library(forecast)
data = IVAE_CP$IVAE_CR %>% ts(start = c(2009,1),frequency = 12)->IVAE_CR
print(IVAE_CR)## 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
## 2022autoplot(IVAE_CR,
xlab = "años",
ylab = "Indice",
main = "IVAE total de Costa Rica, periodo 2009-2022 (AgostO)") +
autolayer(IVAE_CR)
2. Proyeción a seis meses
library(forecast)
modelo<-auto.arima(y = IVAE_CR)
summary(modelo)## Series: IVAE_CR
## 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 -0.068264pronosticos<-forecast(modelo,h = 5)
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$mean))),start = c(2009,1),frequency = 12)
print(IVAE_h)## Jan Feb Mar Apr May Jun Jul Aug
## 2009 73.0500 70.5000 75.6800 70.1300 72.3000 73.4300 72.9300 72.0000
## 2010 75.1000 73.5300 79.9200 73.2700 75.7400 76.4300 76.1300 75.5800
## 2011 78.2700 76.7700 82.0000 76.0300 79.2300 79.6300 77.9900 77.8900
## 2012 82.3700 82.9500 86.0300 78.5500 82.2300 81.8300 80.6000 81.7700
## 2013 83.1000 82.7900 85.6200 81.1300 84.1200 83.7700 83.8800 83.9700
## 2014 86.4100 87.0400 89.1200 83.1200 86.0400 85.3600 86.6300 86.1700
## 2015 88.3000 90.0400 92.8600 88.5000 92.0900 92.5300 93.8400 92.7500
## 2016 94.5300 95.6000 96.3600 93.1300 95.3900 95.6600 94.9400 94.8400
## 2017 96.7100 96.9600 100.8500 94.8400 99.0600 99.9000 96.2600 96.6400
## 2018 99.2100 99.0000 103.5500 99.6200 104.5900 103.4300 101.4600 101.1000
## 2019 101.4800 101.9300 105.9400 99.9800 103.7800 103.6300 102.4500 101.4300
## 2020 102.2000 104.2300 102.6000 89.6500 91.8100 95.7800 91.8600 92.5100
## 2021 96.6300 100.2900 108.0900 101.6600 104.5000 104.7300 107.7700 105.7100
## 2022 106.3100 108.1400 117.4900 105.6100 108.9000 109.1000 110.0600 110.3400
## 2023 108.5389 110.5827
## Sep Oct Nov Dec
## 2009 73.6400 76.7700 78.1800 78.3500
## 2010 77.1400 79.7400 82.1600 81.0600
## 2011 80.0500 83.5700 85.9300 84.6700
## 2012 82.7500 85.6900 89.2600 88.6300
## 2013 86.0400 88.5300 90.7700 90.8000
## 2014 88.1400 92.5500 94.0000 95.2300
## 2015 93.7800 96.6700 98.4300 97.8700
## 2016 98.1200 101.2600 103.9000 103.7900
## 2017 98.9900 103.9600 107.7100 108.1100
## 2018 101.6200 106.0900 108.9000 108.0100
## 2019 103.5700 109.0500 111.4700 111.0900
## 2020 97.3900 101.7200 105.1200 110.6100
## 2021 108.6200 111.2300 116.9100 119.8400
## 2022 110.4800 113.9698 118.1828 120.7984
## 20234. Descomposición de la serie temporal
library(stats)
fit<-stl(IVAE_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)## Jan Feb Mar Apr May Jun Jul
## 2009 73.52619 73.63617 73.74616 73.85928 73.97240 74.09857 74.22474
## 2010 75.56276 75.89925 76.23573 76.55572 76.87570 77.11770 77.35970
## 2011 78.63108 78.90868 79.18628 79.49073 79.79518 80.10528 80.41539
## 2012 82.17307 82.44296 82.71285 82.94654 83.18024 83.33219 83.48415
## 2013 84.20883 84.44363 84.67844 84.90826 85.13808 85.36918 85.60027
## 2014 86.87762 87.08410 87.29059 87.54831 87.80602 88.07348 88.34094
## 2015 90.79751 91.34383 91.89015 92.35186 92.81358 93.19830 93.58303
## 2016 95.28483 95.56641 95.84799 96.23151 96.61503 96.97236 97.32969
## 2017 98.74691 98.91648 99.08605 99.29046 99.49486 99.76727 100.03969
## 2018 101.95680 102.30019 102.64358 102.84681 103.05003 103.18579 103.32154
## 2019 103.65962 103.78169 103.90377 104.09768 104.29160 104.48255 104.67350
## 2020 101.93561 101.06597 100.19634 99.50415 98.81197 98.45198 98.09200
## 2021 102.02801 103.05425 104.08048 105.01028 105.94009 106.77170 107.60332
## 2022 110.65114 110.87178 111.09243 111.26080 111.42917 111.56551 111.70184
## 2023 112.39156 112.51199
## Aug Sep Oct Nov Dec
## 2009 74.37842 74.53210 74.75331 74.97453 75.26865
## 2010 77.54071 77.72172 77.93146 78.14121 78.38614
## 2011 80.72703 81.03867 81.33688 81.63509 81.90408
## 2012 83.54881 83.61346 83.72716 83.84086 84.02484
## 2013 85.84854 86.09680 86.31471 86.53262 86.70512
## 2014 88.62959 88.91825 89.33175 89.74525 90.27138
## 2015 93.92246 94.26188 94.54688 94.83188 95.05836
## 2016 97.57791 97.82613 98.06662 98.30711 98.52701
## 2017 100.29610 100.55252 100.86582 101.17912 101.56796
## 2018 103.42137 103.52119 103.55067 103.58014 103.61988
## 2019 104.64265 104.61181 104.10508 103.59836 102.76698
## 2020 98.26049 98.42899 99.18424 99.93950 100.98376
## 2021 108.31216 109.02100 109.50291 109.98481 110.31797
## 2022 111.81197 111.92210 112.03628 112.15046 112.27101
## 20235. 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(2005,1),frequency = 12)->tabla_coyuntura
print(tail(tabla_coyuntura,n=12))## TC T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## Mar 2018 111.0924 0.19901019 6.737041 8.682382 2.661041 NA
## Apr 2018 111.2608 0.15156076 5.952289 8.704459 2.313518 NA
## May 2018 111.4292 0.15133140 5.181313 8.518255 1.969040 NA
## Jun 2018 111.5655 0.12234949 4.489769 8.171660 1.770370 NA
## Jul 2018 111.7018 0.12219998 3.808914 7.668930 1.572896 NA
## Aug 2018 111.8120 0.09859183 3.231222 7.079024 1.479372 NA
## Sep 2018 111.9221 0.09849472 2.661041 6.404806 NA NA
## Oct 2018 112.0363 0.10201654 2.313518 5.735667 NA NA
## Nov 2018 112.1505 0.10191257 1.969040 5.071054 NA NA
## Dec 2018 112.2710 0.10749316 1.770370 4.458198 NA NA
## Jan 2019 112.3916 0.10737774 1.572896 3.894964 NA NA
## Feb 2019 112.5120 0.10714744 1.479372 3.395621 NA NA6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura %>% 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_coyuntura %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2009,1),frequency = 12) %>% autoplot()