CADFtest {CADFtest}  R Documentation 
This function is an interface to CADFtest.default
that computes the CADF unit root test
proposed in Hansen (1995). The asymptotic pvalues of the test are also computed along the lines
proposed in Costantini et al. (2007). Automatic model selection is allowed. A full description
and some applications can be found in Lupi (2009).
CADFtest(model, X=NULL, type=c("trend", "drift", "none"), data=list(), max.lag.y=1, min.lag.X=0, max.lag.X=0, dname=NULL, criterion=c("none", "BIC", "AIC", "HQC", "MAIC"), ...)
model 
a formula of the kind 
X 
if 
type 
defines the deterministic kernel used in the test. It accepts the values used in package

data 
data to be used (optional). This argument is effective only when 
max.lag.y 
maximum number of lags allowed for the lagged differences of the variable to be tested. 
min.lag.X 
if negative it is maximum lead allowed for the covariates. If zero, it is the minimum lag allowed for the covariates. 
max.lag.X 
maximum lag allowed for the covariates. 
dname 
NULL or character. It can be used to give a special name to the model. If the NULL default is accepted and the model is specified using a formula notation, then dname is computed according to the used formula. 
criterion 
it can be either 
... 
Extra arguments that can be set to use special kernels, prewhitening, etc. in the estimation of
ρ^2. A Quadratic kernel with a VAR(1) prewhitening is the default choice. To set
these extra arguments to different values, see 
The function returns an object of class c("CADFtest", "htest")
containing:
statistic 
the t test statistic. 
parameter 
the estimated nuisance parameter ρ^2 (see Hansen, 1995, p. 1150). 
method 
the test performed: it can be either 
p.value 
the pvalue of the test. 
data.name 
the data name. 
max.lag.y 
the maximum lag of the differences of the dependent variable. 
min.lag.X 
the maximum lead of the stationary covariate(s). 
max.lag.X 
the maximum lag of the stationary covariate(s). 
AIC 
the value of the AIC for the selected model. 
BIC 
the value of the BIC for the selected model. 
HQC 
the value of the HQC for the selected model. 
MAIC 
the value of the MAIC for the selected model. 
est.model 
the estimated model. 
estimate 
the estimated value of the parameter of the lagged dependent variable. 
null.value 
the value of the parameter of the lagged dependent variable under the null. 
alternative 
the alternative hypothesis. 
call 
the call to the function. 
type 
the deterministic kernel used. 
Claudio Lupi
Costantini M, Lupi C, Popp S (2007). A PanelCADF Test for Unit Roots, University of Molise, Economics & Statistics Discussion Paper 39/07. http://econpapers.repec.org/paper/molecsdps/esdp07039.htm
Hansen BE (1995). Rethinking the Univariate Approach to Unit Root Testing: Using Covariates to Increase Power, Econometric Theory, 11(5), 1148–1171.
Lupi C (2009). Unit Root CADF Testing with R, Journal of Statistical Software, 32(2), 1–19. http://www.jstatsoft.org/v32/i02/
Zeileis A (2004). Econometric Computing with HC and HAC Covariance Matrix Estimators, Journal of Statistical Software, 11(10), 1–17. http://www.jstatsoft.org/v11/i10/
Zeileis A (2006). ObjectOriented Computation of Sandwich Estimators, Journal of Statistical Software, 16(9), 1–16. http://www.jstatsoft.org/v16/i09/.
fUnitRoots
, urca
## ADF test on extended NelsonPlosser data  ## Data taken from package urca data(npext, package="urca") ADFt < CADFtest(npext$gnpperca, max.lag.y=3, type="trend") ## CADF test on extended NelsonPlosser data  data(npext, package="urca") npext$unemrate < exp(npext$unemploy) # compute unemployment rate L < ts(npext, start=1860) # time series of levels D < diff(L) # time series of diffs S < window(ts.intersect(L,D), start=1909) # select same sample as Hansen's CADFt < CADFtest(L.gnpperca~D.unemrate, data=S, max.lag.y=3, kernel="Parzen", prewhite=FALSE)