(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Kevin Lewis points us to this paper by Tarun Chordia, Amit Goyal, and Alessio Saretto. I have no disagreement with the substance, but I don’t like their statistical framework with that “false discoveries” thing, as I don’t think there are any true zeros. I believe that most possible trading strategies have very little effect but I doubt the effect is exactly zero, hence I disagree with the premise that there are “type 1 errors.” For the same reason, I don’t like the Bayes factors in the paper. Their whole approach using statistical significance seems awkward to me and I think that in future they’d be able to learn more using multilevel models and forgetting thresholds entirely.
Again, the Chordia et al. paper is fine for what it is; I just think they’re making their life more difficulty by using this indirect hypothesis-testing framework, testing hypotheses that can’t be true and oscillating between the two inappropriate extremes of theta=0 and theta being unconstrained. To me, life’s just to short to mess around like that.
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