Compnet Training Program
CompNet Training Program Structure The course is made for autonomous online learning. It is structured in three modules : Beginners, Intermediate and Advanced. Each of them…
See page
MDI Program
Micro-data Infrastructure (MDI) Training The MDI Training is a three-session program designed to equip researchers (NPBs) with the skills to effectively work with cross-country…
See page
Training
Comprehensive Training Programs: CompNet and MDI Welcome to our Training Programs, designed to empower participants with the knowledge and skills needed for productivity analysis…
See page
Output
Output This page provides an overview of the Output generated as part of the Microdata platform for productivity, which aims to help national productivity boards improve their…
See page
Vocational Training
Vocational Training at IWH At the Halle Institute for Economic Research (IWH) the state-approved professions specialist in media and information services (m/f/x) [library] ,…
See page
Department Profiles
Research Profiles of the IWH Departments All doctoral students are allocated to one of the four research departments (Financial Markets – Laws, Regulations and Factor Markets –…
See page
11th Annual Conference in Luxembourg
11th Annual Conference in Luxembourg 14.-15. September 2022 in Luxembourg This year CompNet celebrated its 11th Annual Conference, together with EIB and ENRI as co-hosts, which…
See page
CompNet-EBRD Workshop
Localization and Productivity CompNet-EBRD Workshop, October 8-9, 2018, European Bank for Reconstruction and Development, London, United Kingdom The workshop of The…
See page
CompNet Data Provider Forum & TSI Workshop
7th CompNet Data Provider Forum & 4th TSI Workshop The upcoming CompNet Data Providers Forum and TSI Workshop, taking place in Amsterdam on November 18-19, 2024, promises to be a…
See page
Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
Econometric Theory,
Vol. 40 (3),
2024
Abstract
This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.
Read article
