Unit Root Tests

February 12, 2014
By

(This article was first published on Freakonometrics » R-english, and kindly contributed to R-bloggers)

This week, in the MAT8181 Time Series course, we’ve discussed unit root tests. According to Wold’s theorem, if http://latex.codecogs.com/gif.latex?(Y_t) is  (weakly) stationnary then

http://latex.codecogs.com/gif.latex?%20%20%20%20%20Y_{t}=\sum%20_{{j=0}}^{\infty%20}\psi_{j}\varepsilon%20_{{t-j}}+\xi%20_{t}

where http://latex.codecogs.com/gif.latex?%20(\varepsilon%20_{{t}}) is the innovation process, and where http://latex.codecogs.com/gif.latex?%20(\xi%20_{{t}}) is some deterministic series (just to get a result as general as possible). Observe that

http://latex.codecogs.com/gif.latex?\sum%20_{{j=0}}^{{\infty%20}}|\psi_{{j}}|^{2}%20%3C%20\infty

as discussed in a previous post. To go one step further, there is also the Beveridge-Nelson decomposition : an integrated of order one process, defined as

http://latex.codecogs.com/gif.latex?%20%20%20%20%20\Delta%20Y_{t}=(1-L)%20Y_t=\sum%20_{{j=0}}^{\infty%20}\psi_{j}\varepsilon%20_{{t-j}}+\xi=\Psi(L)\varepsilon%20_{{t}}+\xican be represented as

a linear trend http://latex.codecogs.com/gif.latex?+ a random walk http://latex.codecogs.com/gif.latex?+ a stationary remaining term

i.e.

http://latex.codecogs.com/gif.latex?%20Y_{t}=\underbrace{Y_0%20+%20\xi%20t%20%C2%A0}+\underbrace{\Psi(1)\sum_{j=1}^t\varepsilon%20_{{i}}}+\underbrace{\tilde\Psi(L)\varepilon_0-\tilde\Psi(L)\varepsilon_t}

where http://latex.codecogs.com/gif.latex?%20\tilde\Psi(\cdot) is the polynomial with terms http://latex.codecogs.com/gif.latex?%20\tilde\psi_j, where

http://latex.codecogs.com/gif.latex?%20\tilde\psi_j%20=\sum_{i=j+1}^\infty\psi_i

For unit-root tests, we will use various representation of the process. In order to illustrate the implementation of those tests, consider the following series

> E=rnorm(240)
> X=cumsum(E)
> plot(X,type="l")
  • Dickey Fuller (standard)

Here, for the simple version of the Dickey-Fuller test, we assume that

http://latex.codecogs.com/gif.latex?%20Y_t=\alpha+\beta%20t+\varphi%20Y_{t-1}+\varepsilon_t

and we would like to test if http://latex.codecogs.com/gif.latex?%20\varphi=1 (or not). We can write the previous representation as

http://latex.codecogs.com/gif.latex?%20\Delta%20Y_t=\alpha+\beta%20t+[\varphi-1]%20Y_{t-1}+\varepsilon_t

so we simply have to test if the regression coefficient in the linear regression is – or not – null. Which can be done with Student’s test. If we consider the previous model without the linear drift, we have to consider the following regression

> lags=0
> z=diff(X)
> n=length(z)
> z.diff=embed(z, lags+1)[,1]
> z.lag.1=X[(lags+1):n]
> summary(lm(z.diff~0+z.lag.1 ))

Call:
lm(formula = z.diff ~ 0 + z.lag.1)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.84466 -0.55723 -0.00494  0.63816  2.54352 

Coefficients:
         Estimate Std. Error t value Pr(>|t|)
z.lag.1 -0.005609   0.007319  -0.766    0.444

Residual standard error: 0.963 on 238 degrees of freedom
Multiple R-squared:  0.002461,	Adjusted R-squared:  -0.00173 
F-statistic: 0.5873 on 1 and 238 DF,  p-value: 0.4442

Our testing procedure will be based on the Student’s t value,

> summary(lm(z.diff~0+z.lag.1 ))$coefficients[1,3]
[1] -0.7663308

which is exactly the value computed using

> library(urca)
> df=ur.df(X,type="none",lags=0)
> df

############################################################### 
# Augmented Dickey-Fuller Test Unit Root / Cointegration Test # 
############################################################### 

The value of the test statistic is: -0.7663

The interpretation of this value can be done using critical values (99%, 95%, 90%)

> qnorm(c(.01,.05,.1)/2)
[1] -2.575829 -1.959964 -1.644854

If the statistics exceeds those values, then the series is not stationnary, since we cannot reject the assumption that http://latex.codecogs.com/gif.latex?%20\varphi-1=0. So we might conclude that there is a unit root. Actually, those critical values are obtained using

> summary(df)

############################################### 
# Augmented Dickey-Fuller Test Unit Root Test # 
############################################### 

Test regression none 

Call:
lm(formula = z.diff ~ z.lag.1 - 1)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.84466 -0.55723 -0.00494  0.63816  2.54352 

Coefficients:
         Estimate Std. Error t value Pr(>|t|)
z.lag.1 -0.005609   0.007319  -0.766    0.444

Residual standard error: 0.963 on 238 degrees of freedom
Multiple R-squared:  0.002461,	Adjusted R-squared:  -0.00173 
F-statistic: 0.5873 on 1 and 238 DF,  p-value: 0.4442

Value of test-statistic is: -0.7663 

Critical values for test statistics: 
      1pct  5pct 10pct
tau1 -2.58 -1.95 -1.62

The problem with R is that there are several packages that can be used for unit root tests. Just to mention another one,

> library(tseries)
> adf.test(X,k=0)

	Augmented Dickey-Fuller Test

data:  X
Dickey-Fuller = -2.0433, Lag order = 0, p-value = 0.5576
alternative hypothesis: stationary

We do have here also a test where the null hypothesis is that there is a unit root. But the p-value is quite different. What is odd is that we have

> 1-adf.test(X,k=0)$p.value
[1] 0.4423705
> df@testreg$coefficients[4]
[1] 0.4442389

(but I think it is a coincidence).

  • Augmented Dickey Fuller

It is possible to had some lags in the regression. For instance, we can consider

http://latex.codecogs.com/gif.latex?%20\Delta%20Y_t=\alpha+\beta%20t+[\varphi-1]%20Y_{t-1}+\psi%20\Delta%20Y_{t-1}+\varepsilon_t

Again, we have to check if one coefficient is null, or not. And this can be done using Student’s t test.

> lags=1
> z=diff(X)
> n=length(z)
> z.diff=embed(z, lags+1)[,1]
> z.lag.1=X[(lags+1):n]
> k=lags+1
> z.diff.lag = embed(z, lags+1)[, 2:k]
> summary(lm(z.diff~0+z.lag.1+z.diff.lag ))

Call:
lm(formula = z.diff ~ 0 + z.lag.1 + z.diff.lag)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.87492 -0.53977 -0.00688  0.64481  2.47556 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
z.lag.1    -0.005394   0.007361  -0.733    0.464
z.diff.lag -0.028972   0.065113  -0.445    0.657

Residual standard error: 0.9666 on 236 degrees of freedom
Multiple R-squared:  0.003292,	Adjusted R-squared:  -0.005155 
F-statistic: 0.3898 on 2 and 236 DF,  p-value: 0.6777

> summary(lm(z.diff~0+z.lag.1+z.diff.lag ))$coefficients[1,3]
[1] -0.7328138

This value is the one obtained using

> df=ur.df(X,type="none",lags=1)
> summary(df)

############################################### 
# Augmented Dickey-Fuller Test Unit Root Test # 
############################################### 

Test regression none 

Call:
lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.87492 -0.53977 -0.00688  0.64481  2.47556 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
z.lag.1    -0.005394   0.007361  -0.733    0.464
z.diff.lag -0.028972   0.065113  -0.445    0.657

Residual standard error: 0.9666 on 236 degrees of freedom
Multiple R-squared:  0.003292,	Adjusted R-squared:  -0.005155 
F-statistic: 0.3898 on 2 and 236 DF,  p-value: 0.6777

Value of test-statistic is: -0.7328 

Critical values for test statistics: 
      1pct  5pct 10pct
tau1 -2.58 -1.95 -1.62

And again, other pckages can be used:

> adf.test(X,k=1)

	Augmented Dickey-Fuller Test

data:  X
Dickey-Fuller = -1.9828, Lag order = 1, p-value = 0.5831
alternative hypothesis: stationary

Hopefully, the conclusion is the same (we should reject the assumption that the series is stationary, but I am not sure about the computation of the p-value).

  • Augmented Dickey Fuller with trend and drift

So far, we have not included the drift in our model. But this is simple to do (this will be called the augmented version of the previous procedure): we just have to include a constant in the regression,

> summary(lm(z.diff~1+z.lag.1+z.diff.lag ))

Call:
lm(formula = z.diff ~ 1 + z.lag.1 + z.diff.lag)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.91930 -0.56731 -0.00548  0.62932  2.45178 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.29175    0.13153   2.218   0.0275 *
z.lag.1     -0.03559    0.01545  -2.304   0.0221 *
z.diff.lag  -0.01976    0.06471  -0.305   0.7603  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9586 on 235 degrees of freedom
Multiple R-squared:  0.02313,	Adjusted R-squared:  0.01482 
F-statistic: 2.782 on 2 and 235 DF,  p-value: 0.06393

The statistics of interest are obtained here considering some analysis of variance outputs, where this model is compared with the one without the integrated part, and the drift,

> summary(lm(z.diff~1+z.lag.1+z.diff.lag ))$coefficients[2,3]
[1] -2.303948
> anova(lm(z.diff ~ z.lag.1 + 1 + z.diff.lag),lm(z.diff ~ 0 + z.diff.lag))$F[2]
[1] 2.732912

Those two values are the ones obtained also with

> df=ur.df(X,type="drift",lags=1)
> summary(df)

############################################### 
# Augmented Dickey-Fuller Test Unit Root Test # 
############################################### 

Test regression drift 

Call:
lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.91930 -0.56731 -0.00548  0.62932  2.45178 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.29175    0.13153   2.218   0.0275 *
z.lag.1     -0.03559    0.01545  -2.304   0.0221 *
z.diff.lag  -0.01976    0.06471  -0.305   0.7603  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9586 on 235 degrees of freedom
Multiple R-squared:  0.02313,	Adjusted R-squared:  0.01482 
F-statistic: 2.782 on 2 and 235 DF,  p-value: 0.06393

Value of test-statistic is: -2.3039 2.7329 

Critical values for test statistics: 
      1pct  5pct 10pct
tau2 -3.46 -2.88 -2.57
phi1  6.52  4.63  3.81

And we can also include a linear trend,

> temps=(lags+1):n
> summary(lm(z.diff~1+temps+z.lag.1+z.diff.lag ))

Call:
lm(formula = z.diff ~ 1 + temps + z.lag.1 + z.diff.lag)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.87727 -0.58802 -0.00175  0.60359  2.47789 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.3227245  0.1502083   2.149   0.0327 *
temps       -0.0004194  0.0009767  -0.429   0.6680  
z.lag.1     -0.0329780  0.0166319  -1.983   0.0486 *
z.diff.lag  -0.0230547  0.0652767  -0.353   0.7243  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9603 on 234 degrees of freedom
Multiple R-squared:  0.0239,	Adjusted R-squared:  0.01139 
F-statistic:  1.91 on 3 and 234 DF,  p-value: 0.1287

> summary(lm(z.diff~1+temps+z.lag.1+z.diff.lag ))$coefficients[3,3]
[1] -1.98282
> anova(lm(z.diff ~ z.lag.1 + 1 + temps+ z.diff.lag),lm(z.diff ~ 1+ z.diff.lag))$F[2]
[1] 2.737086

while R function returns

> df=ur.df(X,type="trend",lags=1)
> summary(df)

############################################### 
# Augmented Dickey-Fuller Test Unit Root Test # 
############################################### 

Test regression trend 

Call:
lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.87727 -0.58802 -0.00175  0.60359  2.47789 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.3227245  0.1502083   2.149   0.0327 *
z.lag.1     -0.0329780  0.0166319  -1.983   0.0486 *
tt          -0.0004194  0.0009767  -0.429   0.6680  
z.diff.lag  -0.0230547  0.0652767  -0.353   0.7243  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9603 on 234 degrees of freedom
Multiple R-squared:  0.0239,	Adjusted R-squared:  0.01139 
F-statistic:  1.91 on 3 and 234 DF,  p-value: 0.1287

Value of test-statistic is: -1.9828 1.8771 2.7371 

Critical values for test statistics: 
      1pct  5pct 10pct
tau3 -3.99 -3.43 -3.13
phi2  6.22  4.75  4.07
phi3  8.43  6.49  5.47
  • KPSS test

Here, in the KPSS testing procedure, two models can be considerd : with a drift, or with a linear trend. Here, the null hypothesis is that the series is stationnary.

With a drift, the code is

> summary(ur.kpss(X,type="mu"))

####################### 
# KPSS Unit Root Test # 
####################### 

Test is of type: mu with 4 lags. 

Value of test-statistic is: 0.972 

Critical value for a significance level of: 
                10pct  5pct 2.5pct  1pct
critical values 0.347 0.463  0.574 0.73

while it will be, in the case there is a trend

> summary(ur.kpss(X,type="tau"))

####################### 
# KPSS Unit Root Test # 
####################### 

Test is of type: tau with 4 lags. 

Value of test-statistic is: 0.5057 

Critical value for a significance level of: 
                10pct  5pct 2.5pct  1pct
critical values 0.119 0.146  0.176 0.216

One more time, it is possible to use another package to get the same test (but again, a different output)

> kpss.test(X,"Level")

	KPSS Test for Level Stationarity

data:  X
KPSS Level = 1.1997, Truncation lag parameter = 3, p-value = 0.01

> kpss.test(X,"Trend")

	KPSS Test for Trend Stationarity

data:  X
KPSS Trend = 0.6234, Truncation lag parameter = 3, p-value = 0.01

At least, there is some kind of consistency, since we keep rejecting the stationnary assumption, for that series.

  • Philipps-Perron test

The Philipps-Perron test is based on the ADF procedure. The code is here

> PP.test(X)

	Phillips-Perron Unit Root Test

data:  X
Dickey-Fuller = -2.0116, Truncation lag parameter = 4, p-value = 0.571

with again, a possible alternative with the other package

> pp.test(X)

	Phillips-Perron Unit Root Test

data:  X
Dickey-Fuller Z(alpha) = -7.7345, Truncation lag parameter = 4, p-value
= 0.6757
alternative hypothesis: stationary
  •  Comparison

I will not spend more time comparing the different codes, in R, to run those tests. Let us spend some additional time on a quick comparison of those three procedure. Let us generate some autoregressive processes, with more or less autocorrelation, as well as some random walk, and let us see how those tests perform :

> n=100
> AR=seq(1,.7,by=-.01)
> P=matrix(NA,3,31)
> M1=matrix(NA,1000,length(AR))
> M2=matrix(NA,1000,length(AR))
> M3=matrix(NA,1000,length(AR))

> for(i in 1:(length(AR)+1)){
+ for(s in 1:1000){
+ if(i==1) X=cumsum(rnorm(n))
+ if(i!=1) X=arima.sim(n=n,list(ar=AR[i]))
+ library(urca)
+ M2[s,i]=as.numeric(pp.test(X)$p.value)
+ M1[s,i]=as.numeric(kpss.test(X)$p.value)
+ M3[s,i]=as.numeric(adf.test(X)$p.value)
+ }}

Here, we would like to count how many times the p-value of our tests exceed 5%,

> prop05=function(x) mean(x>.05)
+ P[1,]=1-apply(M1,2,prop05)
+ P[2,]=apply(M2,2,prop05)
+ P[3,]=apply(M3,2,prop05)
+ }
> plot(AR,P[1,],type="l",col="red",ylim=c(0,1),ylab="proportion of non-stationnary 
+ series",xlab="autocorrelation coefficient")
> lines(AR,P[2,],type="l",col="blue")
> lines(AR,P[3,],type="l",col="green")
> legend(.7,1,c("ADF","KPSS","PP"),col=c("green","red","blue"),lty=1,lwd=1)

 

We can see here how poorly Dickey-Fuller test behave, since a 50% (at least) of our autoregressive processes are considered as non-stationnary.

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