LABORATORIO 3
ALEXANDER DANIEL ALVAREZ BERARDI | AB16003
22/1/2021
Integrantes:
AB16003. Alexander Daniel Alvarez Berardi.
JA13009. Rosa Katya Jovel Barahona.
PO16004. Silvia Raquel Paz Ortiz.
PG15036. Rene Ernesto Pereira Garcia.
CL12025. Jose Manuel Canales Lopez.
1. IMPORTAR LOS DATOS
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts<-read_excel("C:/Users/Alexander Berardi/Desktop/Metodos para el analisis economico/Lab 3/IVAE.doc.xlsx",
col_types = c("skip", "skip", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric"))
data=ivae_ts$` IVAE` %>% ts(start = c(2005,1),frequency = 12)->ivae
print(ivae)## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2005 75.92 75.76 81.87 80.58 88.91 88.37 83.28 84.43 86.20 84.28
## 2006 81.72 80.03 87.53 80.37 90.05 90.58 83.50 86.67 91.30 92.21
## 2007 83.31 77.77 86.95 78.74 93.62 92.29 87.98 91.63 88.35 92.64
## 2008 85.76 85.50 87.67 89.96 93.86 92.26 86.03 91.05 92.21 93.62
## 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 87.19 90.25 88.99 88.73
## 2011 90.26 86.73 94.33 90.80 98.49 97.59 92.16 94.22 92.34 89.07
## 2012 92.66 91.20 98.45 91.22 102.83 102.84 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 94.70 101.29 97.12 103.86 104.73 98.48 98.60 98.25 96.43
## 2015 98.88 94.83 103.16 98.76 105.66 105.46 101.68 101.06 100.63 100.43
## 2016 99.21 97.71 102.52 103.37 107.71 110.69 103.99 106.24 104.85 102.09
## 2017 101.62 99.20 108.68 101.61 111.02 113.76 105.64 107.88 106.01 102.76
## 2018 105.30 103.75 109.16 107.74 112.62 114.34 108.16 111.08 106.38 105.39
## 2019 107.37 106.47 112.71 109.23 115.45 115.61 110.99 113.05 110.86 108.32
## 2020 110.53 111.38 106.03 87.69 89.85 97.23 95.87 101.65 105.08 104.28
## Nov Dec
## 2005 90.61 94.25
## 2006 96.98 94.23
## 2007 97.99 101.28
## 2008 95.41 94.03
## 2009 91.86 99.64
## 2010 93.12 100.76
## 2011 96.87 103.90
## 2012 99.61 105.05
## 2013 101.24 108.36
## 2014 100.65 107.20
## 2015 104.88 109.82
## 2016 106.57 115.13
## 2017 109.94 117.41
## 2018 111.65 119.85
## 2019 116.24 123.44
## 2020autoplot(ivae,xlab = "años",ylab = "Indice",main = "Ivae total, periodo 2005-2020 (octubre)")+theme_bw()
2. PROYECCIÓN A SEIS MESES
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)## Series: ivae
## ARIMA(1,0,2)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 ma1 ma2 sma1 drift
## 0.6723 0.0795 0.1863 -0.7499 0.1400
## s.e. 0.0879 0.1045 0.0968 0.0772 0.0205
##
## sigma^2 estimated as 7.362: log likelihood=-433.12
## AIC=878.25 AICc=878.74 BIC=897.34
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.0005216099 2.589122 1.768711 -0.06752556 1.847341 0.5402127
## ACF1
## Training set 0.009341845pronosticos<-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(2005,1),frequency = 12)
print(ivae_h)## Jan Feb Mar Apr May Jun Jul Aug
## 2005 75.9200 75.7600 81.8700 80.5800 88.9100 88.3700 83.2800 84.4300
## 2006 81.7200 80.0300 87.5300 80.3700 90.0500 90.5800 83.5000 86.6700
## 2007 83.3100 77.7700 86.9500 78.7400 93.6200 92.2900 87.9800 91.6300
## 2008 85.7600 85.5000 87.6700 89.9600 93.8600 92.2600 86.0300 91.0500
## 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 87.1900 90.2500
## 2011 90.2600 86.7300 94.3300 90.8000 98.4900 97.5900 92.1600 94.2200
## 2012 92.6600 91.2000 98.4500 91.2200 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 101.2900 97.1200 103.8600 104.7300 98.4800 98.6000
## 2015 98.8800 94.8300 103.1600 98.7600 105.6600 105.4600 101.6800 101.0600
## 2016 99.2100 97.7100 102.5200 103.3700 107.7100 110.6900 103.9900 106.2400
## 2017 101.6200 99.2000 108.6800 101.6100 111.0200 113.7600 105.6400 107.8800
## 2018 105.3000 103.7500 109.1600 107.7400 112.6200 114.3400 108.1600 111.0800
## 2019 107.3700 106.4700 112.7100 109.2300 115.4500 115.6100 110.9900 113.0500
## 2020 110.5300 111.3800 106.0300 87.6900 89.8500 97.2300 95.8700 101.6500
## 2021 108.7177 107.7527 111.6328 104.8025
## Sep Oct Nov Dec
## 2005 86.2000 84.2800 90.6100 94.2500
## 2006 91.3000 92.2100 96.9800 94.2300
## 2007 88.3500 92.6400 97.9900 101.2800
## 2008 92.2100 93.6200 95.4100 94.0300
## 2009 87.5700 85.2700 91.8600 99.6400
## 2010 88.9900 88.7300 93.1200 100.7600
## 2011 92.3400 89.0700 96.8700 103.9000
## 2012 93.9400 93.4900 99.6100 105.0500
## 2013 97.7400 96.2200 101.2400 108.3600
## 2014 98.2500 96.4300 100.6500 107.2000
## 2015 100.6300 100.4300 104.8800 109.8200
## 2016 104.8500 102.0900 106.5700 115.1300
## 2017 106.0100 102.7600 109.9400 117.4100
## 2018 106.3800 105.3900 111.6500 119.8500
## 2019 110.8600 108.3200 116.2400 123.4400
## 2020 105.0800 104.2800 112.2728 120.0459
## 20214. 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
## 2005 81.68300 82.20308 82.72317 83.20293 83.68268 84.14033 84.59797
## 2006 86.10761 86.32076 86.53391 86.94375 87.35358 87.68576 88.01794
## 2007 88.22274 88.28052 88.33829 88.51946 88.70062 89.14859 89.59656
## 2008 91.22212 91.16829 91.11446 91.07529 91.03612 90.92730 90.81849
## 2009 89.47014 89.17449 88.87884 88.60650 88.33417 88.35138 88.36858
## 2010 89.40749 89.58019 89.75288 89.91342 90.07397 90.23566 90.39734
## 2011 92.45009 92.82840 93.20672 93.44539 93.68406 93.86563 94.04721
## 2012 95.63108 95.98330 96.33553 96.59006 96.84458 96.95572 97.06686
## 2013 97.37757 97.60117 97.82478 98.05542 98.28607 98.51201 98.73795
## 2014 99.98197 100.11160 100.24124 100.23659 100.23193 100.14478 100.05762
## 2015 100.63081 100.93423 101.23764 101.53399 101.83034 102.00267 102.17500
## 2016 103.28678 103.67162 104.05646 104.38950 104.72255 104.96757 105.21259
## 2017 106.12693 106.35014 106.57335 106.78253 106.99170 107.19257 107.39344
## 2018 108.63464 108.88610 109.13756 109.32673 109.51590 109.63467 109.75344
## 2019 110.87674 111.19037 111.50400 111.81467 112.12535 112.36168 112.59802
## 2020 107.20467 105.90932 104.61397 103.84401 103.07405 102.82086 102.56766
## 2021 109.29216 110.61179 111.93143 113.27528
## Aug Sep Oct Nov Dec
## 2005 85.03256 85.46716 85.69144 85.91572 86.01166
## 2006 88.08771 88.15748 88.13550 88.11353 88.16813
## 2007 90.07807 90.55959 90.85473 91.14986 91.18599
## 2008 90.61055 90.40262 90.15151 89.90040 89.68527
## 2009 88.54895 88.72932 88.91527 89.10122 89.25436
## 2010 90.63009 90.86283 91.21965 91.57647 92.01328
## 2011 94.24041 94.43362 94.68985 94.94609 95.28858
## 2012 97.06734 97.06782 97.10220 97.13658 97.25707
## 2013 98.96724 99.19654 99.40497 99.61340 99.79769
## 2014 100.02140 99.98518 100.08626 100.18735 100.40908
## 2015 102.25013 102.32527 102.48845 102.65163 102.96920
## 2016 105.37021 105.52783 105.64707 105.76632 105.94663
## 2017 107.59065 107.78787 107.98363 108.17940 108.40702
## 2018 109.88719 110.02094 110.20055 110.38015 110.62845
## 2019 112.48856 112.37909 111.35176 110.32442 108.76455
## 2020 103.60683 104.64599 105.73800 106.83000 108.06108
## 20215. 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
## May 2020 103.0741 -0.7414566 -8.0724759 -1.739619 -3.1674078 NA
## Jun 2020 102.8209 -0.2456459 -8.4911746 -2.657933 -0.6467786 NA
## Jul 2020 102.5677 -0.2462508 -8.9081157 -3.617699 1.9472032 NA
## Aug 2020 103.6068 1.0131524 -7.8956743 -4.470047 4.4400947 NA
## Sep 2020 104.6460 1.0029906 -6.8812605 -5.216068 6.9947210 NA
## Oct 2020 105.7380 1.0435212 -5.0414662 -5.716548 9.0821471 NA
## Nov 2020 106.8300 1.0327443 -3.1674078 -5.973473 NA NA
## Dec 2020 108.0611 1.1523747 -0.6467786 -5.895052 NA NA
## Jan 2021 109.2922 1.1392463 1.9472032 -5.479561 NA NA
## Feb 2021 110.6118 1.2074347 4.4400947 -4.750034 NA NA
## Mar 2021 111.9314 1.1930296 6.9947210 -3.700043 NA NA
## Apr 2021 113.2753 1.2006032 9.0821471 -2.398105 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(2005,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()