Apple and Intel
aapl = read.csv("AAPL.csv")
intc = read.csv("INTC.csv")
library(forecast)
## Warning: package 'forecast' was built under R version 3.3.2
library(fpp)
## Loading required package: fma
## Warning: package 'fma' was built under R version 3.3.2
## Loading required package: expsmooth
## Loading required package: lmtest
## Warning: package 'lmtest' was built under R version 3.3.2
## Loading required package: zoo
## Warning: package 'zoo' was built under R version 3.3.2
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: tseries
## Warning: package 'tseries' was built under R version 3.3.2
library(vars)
## Loading required package: MASS
##
## Attaching package: 'MASS'
## The following objects are masked from 'package:fma':
##
## cement, housing, petrol
## Loading required package: strucchange
## Loading required package: sandwich
## Warning: package 'sandwich' was built under R version 3.3.2
## Loading required package: urca
combine <- data.frame(aapl$Adj.Close, intc$Adj.Close)
combine.ts <- ts(combine, frequency = 252, start = c(2013,82))
plot(combine.ts)
var1=VARselect(combine.ts, lag.max=8, type="const")$selection
summary(var1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 1.0 1.5 1.5 2.0 2.0
var2=VAR(combine.ts, lag.max=8, type="const")
summary(var2)
##
## VAR Estimation Results:
## =========================
## Endogenous variables: aapl.Adj.Close, intc.Adj.Close
## Deterministic variables: const
## Sample size: 1257
## Log Likelihood: -3148.067
## Roots of the characteristic polynomial:
## 0.9991 0.9931 0.07104 0.03952
## Call:
## VAR(y = combine.ts, type = "const", lag.max = 8)
##
##
## Estimation results for equation aapl.Adj.Close:
## ===============================================
## aapl.Adj.Close = aapl.Adj.Close.l1 + intc.Adj.Close.l1 + aapl.Adj.Close.l2 + intc.Adj.Close.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## aapl.Adj.Close.l1 1.05909 0.03103 34.135 <2e-16 ***
## intc.Adj.Close.l1 -0.18723 0.09960 -1.880 0.0604 .
## aapl.Adj.Close.l2 -0.06139 0.03105 -1.977 0.0483 *
## intc.Adj.Close.l2 0.19207 0.09993 1.922 0.0548 .
## const 0.19155 0.21161 0.905 0.3655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 1.593 on 1252 degrees of freedom
## Multiple R-Squared: 0.9979, Adjusted R-squared: 0.9979
## F-statistic: 1.471e+05 on 4 and 1252 DF, p-value: < 2.2e-16
##
##
## Estimation results for equation intc.Adj.Close:
## ===============================================
## intc.Adj.Close = aapl.Adj.Close.l1 + intc.Adj.Close.l1 + aapl.Adj.Close.l2 + intc.Adj.Close.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## aapl.Adj.Close.l1 0.016520 0.009646 1.713 0.08701 .
## intc.Adj.Close.l1 0.901625 0.030964 29.118 < 2e-16 ***
## aapl.Adj.Close.l2 -0.015140 0.009653 -1.568 0.11705
## intc.Adj.Close.l2 0.092751 0.031065 2.986 0.00288 **
## const 0.050542 0.065787 0.768 0.44248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.4953 on 1252 degrees of freedom
## Multiple R-Squared: 0.9954, Adjusted R-squared: 0.9954
## F-statistic: 6.803e+04 on 4 and 1252 DF, p-value: < 2.2e-16
##
##
##
## Covariance matrix of residuals:
## aapl.Adj.Close intc.Adj.Close
## aapl.Adj.Close 2.5387 0.3248
## intc.Adj.Close 0.3248 0.2454
##
## Correlation matrix of residuals:
## aapl.Adj.Close intc.Adj.Close
## aapl.Adj.Close 1.0000 0.4115
## intc.Adj.Close 0.4115 1.0000
serial.test(var2, lags.pt=10, type="PT.asymptotic")
##
## Portmanteau Test (asymptotic)
##
## data: Residuals of VAR object var2
## Chi-squared = 62.439, df = 32, p-value = 0.001013
fcast=forecast(var2)
plot(fcast)
