Stress Testing with Misspecified Models

Bidder, Rhys; Giacomini, Rafaella; McKenna, Andrew J.

doi:10.24148/wp2016-26

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…We can further restrict the set of permissible priors by including prior or posterior moment restrictions to (2.9)-(2.10), as in the "tilted robustness" problem of Bidder et al (2016). Each additional restriction produces one additional multiplier to solve for, while the moment restriction provides the additional equation with which to solve for the new unknown.…”

Section: Additional Constraintsmentioning

confidence: 99%

Global Robust Bayesian Analysis in Large Models

2019

SSRN Journal

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This paper develops tools for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the framework provides bounds for a wide range of posterior statistics given any prior that is close to the original in relative entropy. The methodology also reveals parts of the prior that are important for the posterior statistics of interest. To implement these calculations in large models, we develop a sequential Monte Carlo algorithm and use approximations to the likelihood and statistic of interest. We use the framework to study error bands for the impulse response of output to a monetary policy shock in the New Keynesian model of Smets and Wouters (2007). The error bands depend asymmetrically on the prior through features of the likelihood that are hard to detect without this formal prior sensitivity analysis.

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Section: Additional Constraintsmentioning

confidence: 99%

Global Robust Bayesian Analysis in Large Models

2019

SSRN Journal

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“…While this is true, any empirical model is likely misspecified in some way. This point is emphasized by Bidder, Giacomini, and McKenna () in the context of the New York Fed's CLASS model—a model used to produce conditional forecasts of bank stress under various severely adverse scenarios. At a minimum, nearly all models are only known up to a collection of unknown parameters that are estimated, which in turn introduces estimation error into the forecast.…”

Section: Introductionmentioning

confidence: 99%

An empirical investigation of direct and iterated multistep conditional forecasts

McCracken

McGillicuddy

2018

J of Applied Econometrics

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Summary When constructing unconditional point forecasts, both direct and iterated multistep (DMS and IMS) approaches are common. However, in the context of producing conditional forecasts, IMS approaches based on vector autoregressions are far more common than simpler DMS models. This is despite the fact that there are theoretical reasons to believe that DMS models are more robust to misspecification than are IMS models. In the context of unconditional forecasts, Marcellino et al. (Journal of Econometrics, 2006, 135, 499–526) investigate the empirical relevance of these theories. In this paper, we extend that work to conditional forecasts. We do so based on linear bivariate and trivariate models estimated using a large dataset of macroeconomic time series. Over comparable samples, our results reinforce those in Marcellino et al.: the IMS approach is typically a bit better than DMS with significant improvements only at longer horizons. In contrast, when we focus on the Great Moderation sample we find a marked improvement in the DMS approach relative to IMS. The distinction is particularly clear when we forecast nominal rather than real variables where the relative gains can be substantial.

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“…Bidder, Giacomini and McKenna (2016) discuss an alternative approach that may improve stress test accuracy.…”

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confidence: 99%