Policy analysis with beliefs about identifying restrictions

Project Information

PABIR

Grant agreement ID: 682877

DOI

10.3030/682877

Project closed

EC signature date 22 August 2016

Start date 1 October 2016

End date 30 September 2022

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 090 686,00

EU contribution

€ 1 090 686,00

Coordinated by

UNIVERSITY COLLEGE LONDON
United Kingdom

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

Reporting period: 2021-04-01 to 2022-09-30

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

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.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

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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