Lag length selection Granger causality test

Consider G-Causality on two stationary time series vectors (call these variables $X$ and $Y$), each with 100+ observations. It's daily financial market time series data. I have reason to believe that there's reverse causality between these two variables (i.e., $X$ causes $Y$ as well as $Y$ causes $X$).

I want to know the current consensus (or nearing consensus) regarding the best method of selecting lag length.

A Google search has only revealed some 1984 and 1985 papers. Scholarpedia's entry on Granger causality says that it's O.K. to minimise AIC or BIC, but no references were provided for this claim so I don't want to code that up until I get confirmation. Or is selection based on qualitative reasoning?

Disclaimer: Cross posted on talkstats.com.

• Disciplinary jargon alert: I would say that "X causes Y as well as Y causes X" is an example of reciprocal causation, rather than reverse causation. In my area, reverse causation means "We postulate that X causes Y, but in actuality Y causes X." – Alexis Mar 11 '15 at 17:39