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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