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 article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hyper-g" specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting "prior inclusion probabilities" according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. (2010) show that inference in Bayesian model averaging (BMA) can be highly sensitive to small changes in the dependent variable. In particular they demonstrate that the importance of growth determinants in explaining growth varies tremendously over different revisions of Penn World Table (PWT) income data. They conclude that 'agnostic' priors appear too sensible for this strand of growth empirics. In response, we show that the instability found owes much to a specific BMA set-up: the variation in results can be considerably reduced by applying an evenly 'agnostic', but flexible prior. Keywords: Bayesian model averaging, Growth determinants, Zellner's g prior, Model uncertainty. JEL Classifications: C11, C15, E01, O47. * The opinions in this paper are those of the authors and do not necessarily coincide with those of the Oesterreichische Nationalbank. We would like to thank Terms of use: Documents in

Non-Technical Summary 4 1. Introduction 6 2. Potential Drivers of External Competitiveness 8 3. Methodology 12 4. Results 17 5. Robustness 20 6. Jointness 21 7. Conclusions 22 Appendix 23 Bibliography 35

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Applied analysis aimed at assessing which value of the NIIP is appropriate for a given country is relatively scarce, and the few existing papers on the topic estimate one-size-fits-all NIIP benchmarks (e.g., Catão and Milesi-Ferretti, 2014). This paper estimates country-specific NIIP benchmarks on a sample of 65 advanced and emerging economies according to two different criteria: consistency with economic fundamentals (NIIP norms, obtained as cumulated current account norms) and prudence against the risk of external crises (NIIP prudential thresholds, obtained as the threshold of the NIIP variable interacted with relative income per capita that maximises signal power in predicting external crises). The median for the country-specific NIIP norms is around-17% of GDP, while the median for prudential threshold is about-44%. The two benchmarks are negatively correlated across countries, highlighting a tension between factors underpinning the scope for external borrowing and debt tolerance. Gaps between actual and NIIP benchmarks are highly persistent, but help predicting subsequent medium-term NIIP changes better than the NIIP level, thus confirming the usefulness of country-specific reference values. The adjustment of the NIIP in response to NIIP gaps is asymmetric, with a significant adjustment limited to negative gaps, with the exception of countries with a positive net position in foreign currency.