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