This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. Default prior choices fixing Zellner's g are predominant in the Bayesian Model Averaging literature, but tend to concentrate posterior mass on a tiny set of models. The paper demonstrates this supermodel effect and proposes to address it by a hyper-g prior, whose data-dependent shrinkage adapts posterior model distributions to data quality. Analytically, existing work on the hyper-g-prior is complemented by posterior expressions essential to fully Bayesian analysis and to sound numerical implementation. A simulation experiment illustrates the implications for posterior inference. Furthermore, an application to determinants of economic growth identifies several covariates whose robustness differs considerably from previous results.
This Policy DiscussionPaper should not be reported as representing the views of the IMF. The views expressed in this Policy Discussion Paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. Policy Discussion Papers describe research in progress by the authors and are published to elicit comments and to further debate.Australia has enjoyed fifteen years of uninterrupted economic expansion since 1992 despite shocks such as the Asian crisis in 1997-98 and the information technology bust in 2000-01. This resilient economic performance owes much to wide-ranging structural reforms and the improved frameworks for monetary and fiscal policies that were implemented after the Australian dollar was floated in 1983. In addition to gaining the expected macroeconomic benefits from exchange rate flexibility, the float appeared to help motivate and facilitate the subsequent reforms. Australia's experience with adapting to a floating currency may therefore be of broader interest.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.