A Model-Averaging Method for Assessing Groundwater Conceptual Model Uncertainty

Vaccine effectiveness (VE) studies are often conducted after the introduction of new vaccines to ensure they provide protection in real-world settings. Although susceptible to confounding, the test-negative case-control study design is the most efficient method to assess VE post-licensure. Control of confounding is often needed during the analyses, which is most efficiently done through multivariable modeling. When a large number of potential confounders are being considered, it can be challenging to know which variables need to be included in the final model. This paper highlights the importance of considering model uncertainty by re-analyzing a Lyme VE study using several confounder selection methods. We propose an intuitive Bayesian Model Averaging (BMA) framework for this task and compare the performance of BMA to that of traditional single-best-model-selection methods. We demonstrate how BMA can be advantageous in situations when there is uncertainty about model selection by systematically considering alternative models and increasing transparency.

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Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

10.31222/osf.io/nshqx ◽

2021 ◽

Author(s):

Tomas Havranek ◽

Roman Horvath ◽

Ali Elminejad

Keyword(s):

Labor Supply ◽

Publication Bias ◽

Model Uncertainty ◽

Model Averaging ◽

Extensive Margin ◽

Real Business Cycle ◽

Intertemporal Substitution ◽

The Mean ◽

Frisch Elasticity ◽

Frequentist Model Averaging

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models and models of taxation. We show that, for the extensive margin elasticity, two biases conspire to systematically produce large positive estimates when the elasticity is in fact zero. Among 723 estimates in 36 studies, the mean reported elasticity is 0.5. One half of that number is due to publication bias: larger estimates are reported preferentially. The other half is due to identification bias: studies with less exogenous time variation in wages report larger elasticities. Net of the biases, the literature implies a zero mean elasticity and, with 95% confidence, is inconsistent with calibrations above 0.25. To derive these results we collect 23 variables that reflect the context in which the elasticity was obtained, use nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to address model uncertainty.

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Using Bayesian model averaging to improve ground motion predictions

Geophysical Journal International ◽

10.1093/gji/ggz486 ◽

2019 ◽

Vol 220 (2) ◽

pp. 1368-1378

Author(s):

M Bertin ◽

S Marin ◽

C Millet ◽

C Berge-Thierry

Keyword(s):

Ground Motion ◽

Model Uncertainty ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Selection Process ◽

Hazard Analysis ◽

Model Averaging ◽

Likelihood Estimation ◽

Predictive Performance ◽

Out Of Sample

SUMMARY In low-seismicity areas such as Europe, seismic records do not cover the whole range of variable configurations required for seismic hazard analysis. Usually, a set of empirical models established in such context (the Mediterranean Basin, northeast U.S.A., Japan, etc.) is considered through a logic-tree-based selection process. This approach is mainly based on the scientist’s expertise and ignores the uncertainty in model selection. One important and potential consequence of neglecting model uncertainty is that we assign more precision to our inference than what is warranted by the data, and this leads to overly confident decisions and precision. In this paper, we investigate the Bayesian model averaging (BMA) approach, using nine ground-motion prediction equations (GMPEs) issued from several databases. The BMA method has become an important tool to deal with model uncertainty, especially in empirical settings with large number of potential models and relatively limited number of observations. Two numerical techniques, based on the Markov chain Monte Carlo method and the maximum likelihood estimation approach, for implementing BMA are presented and applied together with around 1000 records issued from the RESORCE-2013 database. In the example considered, it is shown that BMA provides both a hierarchy of GMPEs and an improved out-of-sample predictive performance.

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