forward, backward, and stepwise confounding variable selection estimate the ATE compared to spike-and-slab Bayesian variable …. Overall, results suggest that Bayesian variable selection is more appealing in smaller sample sizes than frequentist variable …

We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with …

This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the …

We ask whether Bayesian estimation creates a potential estimation bias as compared with standard estimation techniques … treat each in turn as the true model and create Bayesian estimates of it under priors from the true model and its false … alternative. The Bayesian estimation of macro models may thus give very misleading results by placing too much weight on prior …

We propose a blended approach which combines identification via heteroskedasticity with the widely used methods of sign … external instruments can prove necessary when the conditions for point identification through heteroskedasticity are not met … and offers a natural solution to the labeling problem inherent in purely statistical identification strategies. As a …

variables which do not add much information to the inference. The contribution includes a new way of identification which is …

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector …

using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non …

Contrary to claims in studies on financial economics, a sparse database often obscures the identification of parameters … in macroeconomic models. These identification problems originate from the poorly defined mapping between a structural … model and reduced-form parameters. Hence, researchers rely on prominent estimation methods, such as Bayesian approaches …