Proxy vector autoregressions in a data-rich environment

Bruns, Martin (2021) Proxy vector autoregressions in a data-rich environment. Journal of Economic Dynamics and Control, 123. ISSN 0165-1889

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Abstract

I propose a Bayesian approach to identify vector autoregressive (VAR) models via proxies in a data-rich environment. The setup augments a small-scale VAR model with latent factors. It allows to trace out the responses of disaggregated series in a unified model while controlling for broad economic conditions. The posterior sampler accounts for the estimation uncertainty in these latent factors as well as the measurement precision of the proxy. In a first application to monetary policy, I extract factors from a wide range of real and financial series and find that the effects of monetary policy shocks vary along the yield curve. In a second application to oil market shocks I add disaggregated US series to a standard model of the global oil market. I find that negative news about future oil supply have adverse effects on the US economy.

Item Type:	Article
Uncontrolled Keywords:	external instruments,factor-augmented var,monetary policy,oil market shocks,structural var,control and optimization,applied mathematics,economics and econometrics ,/dk/atira/pure/subjectarea/asjc/2600/2606
Faculty \ School:	Faculty of Social Sciences > School of Economics
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance Faculty of Science > Research Groups > Statistics
Related URLs:	http://www.scopus.com/inward/record.url?... https://linkinghub.elsevier.com/retrieve...
Depositing User:	LivePure Connector
Date Deposited:	11 Dec 2020 00:48
Last Modified:	10 Jul 2025 23:55
URI:	https://ueaeprints.uea.ac.uk/id/eprint/77953
DOI:	10.1016/j.jedc.2020.104046

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