Robust Bayesian Analysis for Econometrics

44 Pages Posted: 27 Sep 2021

See all articles by Raffaella Giacomini

Raffaella Giacomini

University College London - Department of Economics; University of California, Los Angeles - Department of Economics

Toru Kitagawa

University College London

Matthew Read

University College London - Department of Economics

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Date Written: August 1, 2021

Abstract

We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the optimal statistical decision under multiple priors. The paper ends with a self-contained discussion of three different approaches to robust Bayesian inference for set- identified structural vector autoregressions, including details about numerical implementation and an empirical illustration.

Suggested Citation

Giacomini, Raffaella and Kitagawa, Toru and Read, Matthew, Robust Bayesian Analysis for Econometrics (August 1, 2021). CEPR Discussion Paper No. DP16488, Available at SSRN: https://ssrn.com/abstract=3928785

Raffaella Giacomini (Contact Author)

University College London - Department of Economics ( email )

Gower Street
London WC1E 6BT, WC1E 6BT
United Kingdom

University of California, Los Angeles - Department of Economics ( email )

405 Hilgard Avenue
Box 951361
Los Angeles, CA 90095-1361
United States

Toru Kitagawa

University College London

Gower Street
London, WC1E 6BT
United Kingdom

Matthew Read

University College London - Department of Economics ( email )

Drayton House, 30 Gordon Street
30 Gordon Street
London, WC1H 0AX
United Kingdom

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