eprintid: 10069216
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/06/92/16
datestamp: 2019-03-04 13:49:00
lastmod: 2021-09-23 22:37:42
status_changed: 2019-03-04 13:49:00
type: article
metadata_visibility: show
creators_name: Jarociński, M
creators_name: Marcet, A
title: Priors about observables in vector autoregressions
ispublished: pub
divisions: UCL
divisions: B03
divisions: C03
divisions: F24
keywords: Bayesian Estimation, Prior Elicitation, Inverse Problem, Structural Vector Autoregression
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2019-04
date_type: published
official_url: http://doi.org/10.1016/j.jeconom.2018.12.023
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1633679
doi: 10.1016/j.jeconom.2018.12.023
lyricists_name: Marcet, Albert
lyricists_id: AMARC54
actors_name: Marcet, Albert
actors_id: AMARC54
actors_role: owner
full_text_status: public
publication: Journal of Econometrics
volume: 209
number: 2
pagerange: 238-255
issn: 1872-6895
citation:        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 <https://doi.org/10.1016/j.jeconom.2018.12.023>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10069216/1/PriorsObservables.pdf