Policy analysis with beliefs about identifying restrictions

Projektinformationen

PABIR

ID Finanzhilfevereinbarung: 682877

DOI

10.3030/682877

Projekt abgeschlossen

EK-Unterschriftsdatum 22 August 2016

Startdatum 1 Oktober 2016

Enddatum 30 September 2022

Finanziert unter

• EXCELLENT SCIENCE - European Research Council (ERC)

Gesamtkosten

€ 1 090 686,00

EU-Beitrag

€ 1 090 686,00

Koordiniert durch

UNIVERSITY COLLEGE LONDON
United Kingdom

Periodic Reporting for period 4 - PABIR (Policy analysis with beliefs about identifying restrictions)

Berichtszeitraum: 2021-04-01 bis 2022-09-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

This project developed a toolkit of new econometric methods for policy analysis using Structural Vector Autoregressions (one of the leading approaches for conducting empirical macroeconomic policy analysis). The methods allow researchers to relax the strong
- and often dubious - assumptions that were previously made in the literature or to formally incorporate beliefs about the validity of such assumptions. The methods rely on Robust Bayesian analysis, which offers computational advantages and a formal way to incorporate beliefs, while removing the sensitivity to assumptions that plagued existing approaches.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The project resulted in several articles published in Econometrica, Journal of Econometrics, Quantitative Economics and Journal of Business and Economic Statistics, which developed the methods and illustrated their empirical relevance.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

One practical implication of the new methods is the suggestion to report set estimators, instead of point estimators. These sets correctly represent what can be learned from the data. The empirical findings in the published articles show that policy conclusions established in the literature can in fact be driven by arbitrary assumptions, thus offering a word of caution to empirical researchers as well as a strong motivation for using the new methods.

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