rank.select(data, lag.max = 10, r.max = ncol(data) - 1,
include = c("const", "trend", "none", "both"),
fitMeasure = c("SSR", "LL"), sameSample = TRUE,
returnModels = FALSE) ## S3 method for class 'rank.select':
print(x, ...)
## S3 method for class 'rank.select':
summary(object, ...)
logLik.VECM.rank.select for the print
method.rank.select for the
summary method.lags.max) and lags (up to
lags.max). This method has been shown to be useful
to select simultaneously the rank and the lags, see
references. -Cheng X and Phillips PCB (2009). Semiparametric
cointegrating rank selection. Econometrics Journal ,
*12*(s1), pp. S83-S104.
- Gonzalo J and Pitarakis J (1998). Specification via
model selection in vector error correction models.
Economics Letters, *60*(3), pp. 321 - 328. ISSN
0165-1765,
- Kapetanios G (2004). The Asymptotic Distribution Of The
Cointegration Rank Estimator Under The Akaike Information
Criterion. Econometric Theory, *20*(04), pp. 735-742.
- Wang Z and Bessler DA (2005). A Monte Carlo Study On
The Selection Of Cointegrating Rank Using Information
Criteria. Econometric Theory, *21*(03), pp. 593-620.
VECM for estimating a VECM.
rank.test (or ca.jo in
package data(barry)
#
rk_sel <- rank.select(barry)
rk_sel
summary(rk_sel)Run the code above in your browser using DataLab