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 …

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

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

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

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 …

In this paper we discuss general identification results for Structural Vector Autoregressions (SVARs) with external … "relevance" and "exogeneity" conditions. We discuss identification results and likelihood-based estimation methods both in the …

due to the presence of unobserved characteristics. This paper studies the identification and estimation of such models. We …

well as an instrument set for the former is considered. Procedures for identification, operating on moments up to a certain … latent regressor. Simple order-conditions are derived, and procedures involving recursive identification of the moments of … Method of Moments (GMM) algorithm involving the instruments and proceeding stepwise from the identification procedures, is …