TY  - JOUR
A1  - Jaroci?ski, M
A1  - Marcet, A
Y1  - 2019/04//
IS  - 2
TI  - Priors about observables in vector autoregressions
KW  - Bayesian Estimation
KW  -  Prior Elicitation
KW  -  Inverse Problem
KW  -  Structural Vector Autoregression
SP  - 238
UR  - http://doi.org/10.1016/j.jeconom.2018.12.023
VL  - 209
N2  - 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.
AV  - public
JF  - Journal of Econometrics
EP  - 255
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SN  - 1872-6895
ID  - discovery10069216
ER  -