But hierarchical models for beta are as old as Vasicek 1973 ( ). Why regularize alpha but not beta? It seems like you could put hierarchical priors on the whole model, why just use the intercepts? Right now the model treats alpha as a known parameter when estimated via OLS. I’m glad people are starting to use hierarchical models in finance again, but I find a few things odd about this paper.ġ. This entry was posted in Bayesian Statistics, Economics, Multilevel Modeling, Sociology by Andrew.

I don’t know anything about the topic and I only glanced at the article, but I’m sharing because of our general interest in the topic of reproducibility in research. The majority of asset pricing factors: (1) can be replicated, (2) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio, (3) work out-of-sample in a new large data set covering 93 countries, and (4) have evidence that is strengthened (not weakened) by the large number of observed factors. We develop and estimate a Bayesian model of factor replication, which leads to different conclusions. Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. The research article, by Theis Ingerslev Jensen, Bryan Kelly, and Lasse Heje Pedersen, begins:

We also make simulations that show how the hierachical model reduces the false discovery rate while sacrificing little power. Indeed, the power of the hierarchical model relative to frequentist multiple-testing adjustment along with simple improvements (e.g., leaving out findings that were never significant in the first place) turns out to make a huge difference here. Inspired by in part by your work on hierachical models, we have analyzed the evidence on research in financial economics, overturning the claims in prior papers that this field faces a replication crisis.

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