Hi Murali

Indeed as you saw, there is one similar implementation in package tsDyn,
rank.select(), that does simultaneous selection of rank and lags using
either AIC or BIC, but does not implement the IC criterion of Philips. Note
however that this simple AIC/BIC procedure has been discussed in quite a
few papers (see refs in ?rank.select), and I haven't heard of papers saying
the Chao and Philips IC being absolutely better than the other
AIC/selection. So why not use that instead?

Best

Matthieu

PS: note that if you manage to write a code for Chao and Philips, I'll be
happy to add it in tsDyn!



2013/2/23 M M <feanor0@hotmail.com>

> Folks,
> I posted this query on r-help and it was suggested that it might be more
> appropriate in this forum.
> Is there any implementation available for the simultaneous selection of
> lag order and rank of a nonstationary VAR as described in Chao & Phillips
> (1999): Model selection in partially nonstationary vector autoregressive
> processes with reduced rank structure, J. Econ. (91).
> (If code in another language is available, I'd be happy to try to
> translate into R.)
> I understand that the usual procedure of first selecting the lag order (by
> AIC, etc.) and then the rank (by Johansen) can result in misspecification
> (small sample problems, overestimation of the cointegration rank, etc.)
> Also, forecasting using parameters from a simultaneous procedure appears to
> be better than using the sequential procedure.
> I would also welcome your thoughts and experiences of using the sequential
> procedure (lag order then rank) in cointegrated VARs (e.g. for forecasting
> economic variables).
>
> Thanks,
>
> Murali
> PS: I found rank.select() in the tsDyn package that does a 'simultaneous'
> choice of rank and lag but differently from Chao/Phillips.
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