Identification through Heterogeneity
Thorsten Drautzburg and
Additional contact information
Pooyan Amir-Ahmadi: University of Illinois at Urbana-Champaign
Authors registered in the RePEc Author Service: Pooyan Amir Ahmadi ()
No 1087, 2017 Meeting Papers from Society for Economic Dynamics
Set identification in Bayesian vector autoregression (VARs) is becoming increasingly popular while facing recent criticism about potentially unwanted prior dominance and underrepresented bounds of the identified set. This can lead to biased inference even in large samples. Common estimation strategies in high dimensions or with tight restrictions can prove to be highly inefficient or even practically infeasible. We propose to include micro data on heterogeneous entities for the estimation and identification of vector autoregressions to achieve sharper inference. First, we provide conditions when imposing a simple ranking of impulse responses will sharpen inference in bivariate and trivariate VARS. Importantly, we show that this sharpening also applies to variables not subject to ranking restrictions. Second, we develop two types of inference to address recent criticism: (i) A prior-robust posterior over the bounds of the identified set and (ii) a fully Bayesian sampling algorithm that allows us to efficiently include an agnostic prior over the non-identifiable parameters. Third, we apply our methodology to US data to identify productivity news and defense spending shocks. We find that under both algorithms the bounds of the identified sets shrink substantially under heterogeneity restrictions relative to standard sign restrictions.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
Working Paper: Identification through Heterogeneity (2017)
Working Paper: IDENTIFICATION THROUGH HETEROGENEITY (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:red:sed017:1087
Access Statistics for this paper
More papers in 2017 Meeting Papers from Society for Economic Dynamics Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Christian Zimmermann ().