rank.test {tsDyn}

Test of the cointegrating rank

Description

Maximum-likelihood test of the cointegrating rank.

Usage

rank.test(vecm, type = c("eigen", "trace"), r_null, cval = 0.05)

## S3 method for class 'rank.test'
print(x, ...)

## S3 method for class 'rank.test'
summary(object, digits = max(1, getOption("digits") - 3), ...)

Arguments

vecm

‘VECM’ object computed with the function VECM.

type

Type of test, either 'trace' or 'eigenvalue'. See details below.

r_null

Rank to test specifically.

cval

Critical value level for the automatic test.

x

The output from rank.test for the print method.

...

Unused.

object

The output from rank.test for the summary method.

digits

The number of digits to use in format.pval

Details

This function computes the two maximum-likelihood tests for the cointegration rank from Johansen (1996). Tests are:

trace

Test the hypothesis of rank ‘h’ against rank ‘K’, i.e. against the alternative that the system is stationary.

eigenvalue

Test the hypothesis of rank ‘h’ against rank ‘h+1’.

The test works for five specifications of the deterministic terms as in Doornik et al (1998), to be specified in the previous call to VECM:

H_ql

Unrestricted constant and trend: use include="both"

H_l

Unrestricted constant and restricted trend: use include="const"

and LRinclude="trend"

H_lc

Unrestricted constant and no trend: use include="const"

H_c

Restricted constant and no trend: use LRinclude="const"

H_z

No constant nor trend: use include="none"

Two testing procedures can be used:

Specific test

By specifying a value for ‘r_null’. The ‘pval’ value returned gives the speciifc p-value.

Automatic test

If not value is specified for ‘r_null’, the function makes a simple automatic test: returns the rank (slot ‘r’) of the first test not rejected (level specified by arg cval) as recommend i.a. in Doornik et al (1998, p. 544).

A full table with both test statistics ad their respective p-values is given in the summary method.

P-values are obtained from the gamma approximation from Doornik (1998, 1999). Small sample values adjusted for the sample site are also available in the summary method. Note that the ‘effective sample size’ for the these values is different from output in gretl for example.

Value

An object of class ‘rank.test’, with ‘print’ and ‘summary methods’.

Comparison with urca

While ca.jo in package urca and rank.test both implement Johansen tests, there are a few differences:

• rank.test gives p-values, while ca.jo gives only critical values.

• rank.test allows for five different specifications of deterministic terms (see above), ca.jo for only three.

• ca.jo allows for seasonal and exogenous regressors, which is not available in rank.test.

• The lag is specified differently: K from ca.jo corresponds to lag+1 in rank.test.

Author(s)

Matthieu Stigler

References

- Doornik, J. A. (1998) Approximations to the Asymptotic Distributions of Cointegration Tests, Journal of Economic Surveys, 12, 573-93

- Doornik, J. A. (1999) Erratum [Approximations to the Asymptotic Distribution of Cointegration Tests], Journal of Economic Surveys, 13, i

- Doornik, Hendry and Nielsen (1998) Inference in Cointegrating Models: UK M1 Revisited, Journal of Economic Surveys, 12, 533-72

- Johansen, S. (1996) Likelihood-based inference in cointegrated Vector Autoregressive Models, Oxford University Press

See Also

VECM for estimating a VECM. rank.select to estimate the rank based on information criteria.

ca.jo in package urca for another implementation of Johansen cointegration test (see section ‘Comparison with urca’ for more infos).

Examples

data(barry)

## estimate the VECM with Johansen! 
ve <- VECM(barry, lag=1, estim="ML")

## specific test:
ve_test_spec <- rank.test(ve, r_null=1)
ve_test_spec_tr <- rank.test(ve, r_null=1, type="trace")

ve_test_spec
ve_test_spec_tr

## No specific test: automatic method
ve_test_unspec <- rank.test(ve)
ve_test_unspec_tr <- rank.test(ve, type="trace")

ve_test_unspec
ve_test_unspec_tr

## summary method: output will be same for all types/test procedure:
summary(ve_test_unspec_tr)

## The function works for many specification of the VECM(), try:
rank.test(VECM(barry, lag=3, estim="ML"))
rank.test(VECM(barry, lag=3, include="both",estim="ML"))
rank.test(VECM(barry, lag=3, LRinclude="const",estim="ML"))

## Note that the tests are simple likelihood ratio, and hence can be obtained also manually:
-2*(logLik(ve, r=1)-logLik(ve, r=2)) # eigen test, 1 against 2
-2*(logLik(ve, r=1)-logLik(ve, r=3)) # eigen test, 1 against 3