Getting the data

library(quantmod)
## Loading required package: xts
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following object is masked from 'package:tsibble':
## 
##     index
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## 
## Attaching package: 'xts'
## The following objects are masked from 'package:dplyr':
## 
##     first, last
## Loading required package: TTR
aa <- getSymbols("AAL", auto.assign=FALSE, from="2014-01-01", to="2019-12-31")
## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
## 
## This message is shown once per session and may be disabled by setting 
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.
dis <- getSymbols("DIS", auto.assign=FALSE, from="2014-01-01", to="2019-12-31")

#aa<-as.data.frame(aa)
#dis<-as.data.frame(dis)


#month_average_aa<- aa %>% group_by(month=floor_date(as.Date(F_FECHAAVALUO), "month")) #%>%summarize(valor=mean(AREA_PRIVADA_VALM2FINAL))
library(quantmod)

AA_DIS<-data.frame(aa$AAL.Adjusted,dis$DIS.Adjusted)
AA_DIS_TS <-ts(AA_DIS)
AA_DIS_TS %>% autoplot()

### MODEL

library(vars)
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following objects are masked from 'package:fma':
## 
##     cement, housing, petrol
## The following object is masked from 'package:dplyr':
## 
##     select
## Loading required package: strucchange
## Loading required package: sandwich
## Loading required package: urca
## Loading required package: lmtest
## 
## Attaching package: 'vars'
## The following object is masked from 'package:fable':
## 
##     VAR
VAR <- VAR(AA_DIS_TS)
summary(VAR)
## 
## VAR Estimation Results:
## ========================= 
## Endogenous variables: AAL.Adjusted, DIS.Adjusted 
## Deterministic variables: const 
## Sample size: 1508 
## Log Likelihood: -4396.511 
## Roots of the characteristic polynomial:
## 0.9994 0.9872
## Call:
## VAR(y = AA_DIS_TS)
## 
## 
## Estimation results for equation AAL.Adjusted: 
## ============================================= 
## AAL.Adjusted = AAL.Adjusted.l1 + DIS.Adjusted.l1 + const 
## 
##                  Estimate Std. Error t value Pr(>|t|)    
## AAL.Adjusted.l1  0.989023   0.003339 296.227  < 2e-16 ***
## DIS.Adjusted.l1 -0.003730   0.001359  -2.744 0.006147 ** 
## const            0.811017   0.213164   3.805 0.000148 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 0.8745 on 1505 degrees of freedom
## Multiple R-Squared: 0.9842,  Adjusted R-squared: 0.9842 
## F-statistic: 4.689e+04 on 2 and 1505 DF,  p-value: < 2.2e-16 
## 
## 
## Estimation results for equation DIS.Adjusted: 
## ============================================= 
## DIS.Adjusted = AAL.Adjusted.l1 + DIS.Adjusted.l1 + const 
## 
##                  Estimate Std. Error t value Pr(>|t|)    
## AAL.Adjusted.l1 -0.005087   0.004926  -1.033    0.302    
## DIS.Adjusted.l1  0.997572   0.002005 497.428   <2e-16 ***
## const            0.494232   0.314480   1.572    0.116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 1.29 on 1505 degrees of freedom
## Multiple R-Squared: 0.9943,  Adjusted R-squared: 0.9943 
## F-statistic: 1.317e+05 on 2 and 1505 DF,  p-value: < 2.2e-16 
## 
## 
## 
## Covariance matrix of residuals:
##              AAL.Adjusted DIS.Adjusted
## AAL.Adjusted       0.7648       0.3173
## DIS.Adjusted       0.3173       1.6646
## 
## Correlation matrix of residuals:
##              AAL.Adjusted DIS.Adjusted
## AAL.Adjusted       1.0000       0.2812
## DIS.Adjusted       0.2812       1.0000
VAR_fc <- VAR %>% forecast(h=120)
VAR_fc %>% autoplot(tsla)