Week-6-Discussion.R

library(forecast)

## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

## Registered S3 methods overwritten by 'forecast':
##   method             from    
##   fitted.fracdiff    fracdiff
##   residuals.fracdiff fracdiff

library(fpp2)

## Loading required package: ggplot2

## Loading required package: fma

## Loading required package: expsmooth

library(vars)

## Warning: package 'vars' was built under R version 3.6.1

## Loading required package: MASS

## 
## Attaching package: 'MASS'

## The following objects are masked from 'package:fma':
## 
##     cement, housing, petrol

## Loading required package: strucchange

## Warning: package 'strucchange' was built under R version 3.6.1

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 3.6.1

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: sandwich

## Loading required package: urca

## Loading required package: lmtest

library(readr)
SPY <- read_csv("C:/Users/Joseph/Downloads/SPY (2).csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   `Adj Close` = col_double()
## )

DJI <- read_csv("C:/Users/Joseph/Downloads/^DJI.csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   `Adj Close` = col_double()
## )

usMarkets <- merge(
 x=SPY,
 y=DJI,
 by='Date'
)

names(usMarkets)[2:3] <- c('SPY', 'DJI')
usMarkets <- ts(usMarkets[,2:3],start=c(2014,9),end=c(2019,9),frequency = 12)
VARselect(usMarkets[,1:2], lag.max=8)

## $selection
## AIC(n)  HQ(n)  SC(n) FPE(n) 
##      7      7      5      7 
## 
## $criteria
##                   1            2            3            4            5
## AIC(n) 1.917145e+01 1.784265e+01 1.741549e+01 1.729874e+01 1.624378e+01
## HQ(n)  1.925722e+01 1.798561e+01 1.761563e+01 1.755607e+01 1.655829e+01
## SC(n)  1.939450e+01 1.821440e+01 1.793595e+01 1.796790e+01 1.706164e+01
## FPE(n) 2.119140e+08 5.616328e+07 3.671130e+07 3.278189e+07 1.147888e+07
##                   6            7            8
## AIC(n) 1.610500e+01 1.598794e+01 1.607409e+01
## HQ(n)  1.647669e+01 1.641682e+01 1.656014e+01
## SC(n)  1.707156e+01 1.710320e+01 1.733805e+01
## FPE(n) 1.007350e+07 9.062976e+06 1.002928e+07

var1 <- VAR(usMarkets[,1:2], p=5, type="const")
serial.test(var1, lags.pt=10, type="PT.asymptotic")

## 
##  Portmanteau Test (asymptotic)
## 
## data:  Residuals of VAR object var1
## Chi-squared = 32.978, df = 20, p-value = 0.03393

var2 <- VAR(usMarkets[,1:2], p=7, type="const")
serial.test(var2, lags.pt=10, type="PT.asymptotic")

## 
##  Portmanteau Test (asymptotic)
## 
## data:  Residuals of VAR object var2
## Chi-squared = 18.837, df = 12, p-value = 0.09253

forecast(var2) %>%
 autoplot() + xlab("Year")