# Tutorial 7: Vector Autoregression

In this tutorial, we will generate bivariate autocorrelated series, we will apply a system-wide information criterion to select a suitable vector autoregressive model, we will perform an in-sample test of Granger causality, we will obtain and compare one-step-ahead forecasts from competing models using a rolling window procedure, and in so doing we will investigate evidence of out-of-sample Granger causality. To run the code, the data.table, ggplot2 and MASS packages need to be installed and loaded.

Let’s generate a two-dimensional vector of time series that follow a VAR(1) process of the following form: \begin{aligned} x_{1t} &= 0.3 + 0.7x_{1t-1} + 0.1x_{2t-1} + \varepsilon_{1t} \\ x_{2t} &= -0.2 + 0.9x_{1t-1} + \varepsilon_{2t} \end{aligned} where $$\mathbf{e}_{t} \sim N(\mathbf{0},\Sigma)$$, and where $$\Sigma$$ is the covariance matrix of the residuals such that $$Cov(\varepsilon_{1,t},\varepsilon_{2,t}) = 0.3$$ for all $$t=1,\ldots,180$$. (Note: in the code, $$x_1$$ is denoted by $$y$$ and $$x_2$$ is denoted by $$x$$).

n <- 180

R <- matrix(c(1,0.3,0.3,1),nrow=2,ncol=2)
set.seed(1)
r <- mvrnorm(n,mu=c(0,0),Sigma=R)

r1 <- r[,1]
r2 <- r[,2]

x1 <- rep(NA,n)
x2 <- rep(NA,n)

x1[1] <- r1[1]
x2[1] <- r2[1]

for(i in 2:n){
x1[i] <-  0.3+0.7*x1[i-1]+0.1*x2[i-1]+r1[i]
x2[i] <- -0.2+0.9*x2[i-1]+r2[i]
}

Generate a vector of some arbitrary dates (e.g., suppose we deal with the monthly series beginning from January 2006), and store these along with $$y$$ in a data.table, call it ‘dt’.

date <- seq(as.Date("2006-01-01"),by="month",along.with=x1)

dt <- data.table(date,x1,x2)

Plot the realized time series using a ggplot function. Before plotting, first ‘melt’ the dataset into the ‘long’ format.

dl <- melt(dt,id.vars="date",variable.name="series",value.name="values")

ggplot(dl,aes(x=date,y=values,color=series,linetype=series))+
geom_line(size=1)+
scale_color_manual(values=c("powderblue","coral"))+
labs(x="Year",y="Series")+
theme_classic()

Estimate VAR(1) and VAR(2) by running regressions on each equation separately. Collect residuals and obtain system-wide AIC for each of the two models.

dt[,:=(x11=shift(x1,1),x12=shift(x1,2),x21=shift(x2,1),x22=shift(x2,2))]

# VAR(1)
p <- 1
k <- 2

var1.x1 <- lm(x1~x11+x21,data=dt)
var1.x2 <- lm(x2~x11+x21,data=dt)

res1 <- cbind(var1.x1$residuals,var1.x2$residuals)
cov1 <- crossprod(res1)/(nrow(dt)-(p*k^2+k))

AIC1 <- log(det(cov1))+2*(p*k^2+k)/nrow(dt)

# VAR(2)
p <- 2
k <- 2

var2.x1 <- lm(x1~x11+x21+x12+x22,data=dt)
var2.x2 <- lm(x2~x11+x21+x12+x22,data=dt)

res2 <- cbind(var2.x1$residuals,var2.x2$residuals)
cov2 <- crossprod(res2)/(nrow(dt)-(p*k^2+k))

AIC2 <- log(det(cov2))+2*(p*k^2+k)/nrow(dt)

AIC1
## [1] -0.1270596
AIC2
## [1] -0.047212

Perform tests of (in-sample) Granger causality in each of the two models. Note, in the case of VAR(1), t tests and F tests are equivalently applicable. In the case of VAR(p), i.e., when $$p>1$$, the only appropriate test is an F test for joint significance of the parameters associated with the lags of the potentially causal (in Garanger sense) variable.

# VAR(1)

## t test
summary(var1.x1)
##
## Call:
## lm(formula = x1 ~ x11 + x21, data = dt)
##
## Residuals:
##      Min       1Q   Median       3Q      Max
## -2.32279 -0.63680 -0.00953  0.68826  2.63468
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.34926    0.11146   3.134  0.00202 **
## x11          0.68903    0.05877  11.725  < 2e-16 ***
## x21          0.11304    0.04509   2.507  0.01308 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.976 on 176 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.5689, Adjusted R-squared:  0.564
## F-statistic: 116.1 on 2 and 176 DF,  p-value: < 2.2e-16
summary(var1.x2)
##
## Call:
## lm(formula = x2 ~ x11 + x21, data = dt)
##
## Residuals:
##      Min       1Q   Median       3Q      Max
## -2.38491 -0.68087  0.03216  0.66109  2.74762
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.22353    0.10805  -2.069    0.040 *
## x11          0.05814    0.05697   1.021    0.309
## x21          0.85285    0.04371  19.511   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9461 on 176 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.7536, Adjusted R-squared:  0.7508
## F-statistic: 269.2 on 2 and 176 DF,  p-value: < 2.2e-16
## F test
ar1.x1 <- lm(x1~x11,data=dt)
ar1.x2 <- lm(x2~x21,data=dt)

anova(var1.x1,ar1.x1)
## Analysis of Variance Table
##
## Model 1: x1 ~ x11 + x21
## Model 2: x1 ~ x11
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)
## 1    176 167.64
## 2    177 173.62 -1   -5.9866 6.2852 0.01308 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(var1.x2,ar1.x2)
## Analysis of Variance Table
##
## Model 1: x2 ~ x11 + x21
## Model 2: x2 ~ x21
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    176 157.54
## 2    177 158.47 -1  -0.93231 1.0416 0.3089
## VAR(2)

### t test (no longer applicable to test GC)
summary(var1.x1)
##
## Call:
## lm(formula = x1 ~ x11 + x21, data = dt)
##
## Residuals:
##      Min       1Q   Median       3Q      Max
## -2.32279 -0.63680 -0.00953  0.68826  2.63468
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.34926    0.11146   3.134  0.00202 **
## x11          0.68903    0.05877  11.725  < 2e-16 ***
## x21          0.11304    0.04509   2.507  0.01308 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.976 on 176 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.5689, Adjusted R-squared:  0.564
## F-statistic: 116.1 on 2 and 176 DF,  p-value: < 2.2e-16
summary(var1.x2)
##
## Call:
## lm(formula = x2 ~ x11 + x21, data = dt)
##
## Residuals:
##      Min       1Q   Median       3Q      Max
## -2.38491 -0.68087  0.03216  0.66109  2.74762
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.22353    0.10805  -2.069    0.040 *
## x11          0.05814    0.05697   1.021    0.309
## x21          0.85285    0.04371  19.511   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9461 on 176 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.7536, Adjusted R-squared:  0.7508
## F-statistic: 269.2 on 2 and 176 DF,  p-value: < 2.2e-16
### F test
ar2.x1 <- lm(x1~x11+x12,data=dt)
ar2.x2 <- lm(x2~x21+x22,data=dt)

anova(var2.x1,ar2.x1)
## Analysis of Variance Table
##
## Model 1: x1 ~ x11 + x21 + x12 + x22
## Model 2: x1 ~ x11 + x12
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)
## 1    173 166.68
## 2    175 172.78 -2   -6.0971 3.1641 0.04471 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(var2.x2,ar2.x2)
## Analysis of Variance Table
##
## Model 1: x2 ~ x11 + x21 + x12 + x22
## Model 2: x2 ~ x21 + x22
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    173 156.76
## 2    175 158.03 -2   -1.2729 0.7024 0.4968

Generate a sequence of one-step-ahead forecasts from VAR(1) using the rolling window scheme, where the first rolling window ranges from period 1 to period 120.

R <- 120
P <- nrow(dt)-R

dt$f.a11 <- NA dt$f.a12 <- NA
dt$f.v11 <- NA dt$f.v12 <- NA

for(i in 1:P){

ar1.x1 <- lm(x1~x11,data=dt[i:(R-1+i)])
ar1.x2 <- lm(x2~x21,data=dt[i:(R-1+i)])

var1.x1 <- lm(x1~x11+x21,data=dt[i:(R-1+i)])
var1.x2 <- lm(x2~x11+x21,data=dt[i:(R-1+i)])

dt$f.a11[R+i] <- ar1.x1$coefficients["(Intercept)"]+ar1.x1$coefficients["x11"]*dt$x1[R-1+i]
dt$f.a12[R+i] <- ar1.x2$coefficients["(Intercept)"]+ar1.x2$coefficients["x21"]*dt$x2[R-1+i]

dt$f.v11[R+i] <- var1.x1$coefficients["(Intercept)"]+var1.x1$coefficients["x11"]*dt$x1[R-1+i]+var1.x1$coefficients["x21"]*dt$x2[R-1+i]
dt$f.v12[R+i] <- var1.x2$coefficients["(Intercept)"]+var1.x2$coefficients["x11"]*dt$x1[R-1+i]+var1.x2$coefficients["x21"]*dt$x2[R-1+i]

}

Calculate the RMSFE measures for restricted and unrestricted models, and compare those to each other to make a suggestion about out-of-sample Granger causality.

dt[,:=(e.a11=x1-f.a11,e.a12=x2-f.a12,e.v11=x1-f.v11,e.v12=x2-f.v12)]

# calculate RMSFEs for restricted and unrestricted models
rmsfe_x1.r <- sqrt(mean(dt$e.a11^2,na.rm=T)) rmsfe_x1.u <- sqrt(mean(dt$e.v11^2,na.rm=T))

rmsfe_x2.r <- sqrt(mean(dt$e.a12^2,na.rm=T)) rmsfe_x2.u <- sqrt(mean(dt$e.v12^2,na.rm=T))

rmsfe_x1.r
## [1] 1.134867
rmsfe_x1.u
## [1] 1.104268
rmsfe_x2.r
## [1] 1.00908
rmsfe_x2.u
## [1] 1.011653