Bayesian econometrics in St. Andrews

A call I received for the incoming 2019 edition of the European Seminar on Bayesian Econometrics (ESOBE), sponsored by the EFaB section of ISBA, which is going to be held at the University of St Andrews in Scotland on Monday 2 and Tuesday 3 September, 2019. I have attended an earlier edition in Venezia and enjoyed it very much. Plus, summer in Scotland…, where else?! Submission of papers is still open:

We aim to have a balance of keynotes from both statistics and econometrics, in order to stimulate submissions from statisticians working on Bayesian methodology or applications in economics/finance. We particularly welcome submissions from young Bayesians (PhDs, PostDocs, assistant professors — EFaB funds a “young researcher session” with up to $500 per speaker).

back to ca’ Foscari, Venezia

I am off to Venezia this afternoon for a Franco-Italian workshop organised by my friends Monica Billio, Roberto Casarin, and Matteo Iacopini, from the Department of Economics of Ca’ Foscari, almost exactly a year after my previous trip there for ESOBE 2016. (Except that this was before!) Tomorrow, I will give both a tutorial [for the second time in two weeks!] and a talk on ABC, hopefully with some portion of the audience still there for the second part!

Ponte de Ca’ Marcello [jatp]

non-identifiability in Venezia

Last Wednesday, I attended a seminar by T. Kitagawa at the economics seminar of the University Ca’ Foscari, in Venice, which was about (uncertain) identifiability and a sort of meta-Bayesian approach to the problem. Just to give an intuition about the setting, a toy example is a simultaneous equation model Ax=ξ, where x and ξ are two-dimensional vectors, ξ being a standard bivariate Normal noise. In that case, A is not completely identifiable. The argument in the talk (and the paper) is that the common Bayesian answer that sets a prior on the non-identifiable part (which is an orthogonal matrix in the current setting) is debatable as it impacts inference on the non-identifiable parts, even in the long run. Which seems fine from my viewpoint. The authors propose to instead consider the range of possible priors that are compatible with the set restrictions on the non-identifiable parts and to introduce a mixture between a regular prior on the whole parameter A and this collection of priors, which can be seen as a set-valued prior although this does not fit within the Bayesian framework in my opinion. Once this mixture is constructed, a formal posterior weight on the regular prior can be derived. As well as a range of posterior values for all quantities of interest. While this approach connects with imprecise probabilities à la Walley (?) and links with robust Bayesian studies of the 1980’s, I always have difficulties with the global setting of such models, which do not come under criticism while being inadequate. (Of course, there are many more things I do not understand in econometrics!)

tre archi [jatp]