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Priors about observables in vector autoregressions

Jarociński, M; Marcet, A; (2019) Priors about observables in vector autoregressions. Journal of Econometrics , 209 (2) pp. 238-255. 10.1016/j.jeconom.2018.12.023. Green open access

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Abstract

Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how to translate the prior on observables into a prior on parameters using strict probability theory principles, a posterior can then be formed with standard procedures. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations. We prove equivalence to a fixed point formulation and a convergence theorem for the algorithm. We use this framework in two well known applications in the VAR literature, we show how priors on observables can address some weaknesses of standard priors, serving as a cross check and an alternative formulation.

Type: Article
Title: Priors about observables in vector autoregressions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jeconom.2018.12.023
Publisher version: http://doi.org/10.1016/j.jeconom.2018.12.023
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bayesian Estimation, Prior Elicitation, Inverse Problem, Structural Vector Autoregression
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10069216
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