We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the impulse responses or forecast error variance decompositions of interest are set-identified using external instruments (or 'proxy SVARs'). Existing Bayesian approaches to inference in proxy...

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...

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose...

We compare two approaches to using information about the signs of structural shocks at specific dates within a structural vector autoregression (SVAR): imposing ‘narrative restrictions’ (NR) on the shock signs in an otherwise set-identified SVAR; and casting the information about the shock...

We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss...