05879oam 22013934 450 991096180250332120251116163655.0978661284409697814623446661462344666978145187349814518734929781452769233145276923097812828440941282844091(CKB)3170000000055349(SSID)ssj0000940071(PQKBManifestationID)11491949(PQKBTitleCode)TC0000940071(PQKBWorkID)10946531(PQKB)11326867(OCoLC)648819742(IMF)WPIEE2009202(MiAaPQ)EBC1608830(IMF)WPIEA2009202WPIEA2009202(EXLCZ)99317000000005534920020129d2009 uf 0engurcn|||||||||txtccrBenchmark Priors Revisited : On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging /Martin Feldkircher, Stefan Zeugner1st ed.Washington, D.C. :International Monetary Fund,2009.39 p. col. illIMF Working Papers"September 2009."9781451917710 1451917716 Includes bibliographical references.Cover Page -- Title Page -- Copyright Page -- Contents -- I Introduction -- II Bayesian Model Averaging under Zellner's g Prior -- II. 1 Popular Settings for Zellner's g -- III The Hyper-g Prior: A Beta Prior on the Shrinkage Factor -- IV A Simulation Study -- V Growth Determinants Revisited -- VI Concluding Remarks -- A Technical Appendix -- A. 1 Consistency of the Hyper-g Prior -- A. 2 Relationship between Hyper-g Prior and EBL -- A. 3 The Shrinkage Factor and Goodness-of-Fit -- A. 4 The Posterior Predictive Distribution and the Hyper-g Prior -- A. 5 The Beta-binomial Prior over the Model Space -- A. 6 Charts and Tables -- References -- Footnotes.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.IMF Working Papers; Working Paper ;No. 2009/202Bayesian statistical decision theoryEconomic developmentMathematical modelsBayesian Analysis: GeneralimfBayesian inferenceimfBayesian modelsimfComputer Programs: GeneralimfData capture & analysisimfData Collection and Data Estimation MethodologyimfData ProcessingimfData processingimfDeflationimfEconometric modelsimfEconometricsimfElectronic data processingimfHuman CapitalimfHuman capitalimfIncome economicsimfInflationimfLabor ProductivityimfLaborimfLabourimfMacroeconomicsimfNational Government Expenditures and Related Policies: InfrastructuresimfOccupational ChoiceimfOther Public Investment and Capital StockimfPrice LevelimfPricesimfPublic finance & taxationimfPublic FinanceimfPublic investment and public-private partnerships (PPP)imfPublic-private sector cooperationimfSkillsimfBayesian statistical decision theory.Economic developmentMathematical models.Bayesian Analysis: GeneralBayesian inferenceBayesian modelsComputer Programs: GeneralData capture & analysisData Collection and Data Estimation MethodologyData ProcessingData processingDeflationEconometric modelsEconometricsElectronic data processingHuman CapitalHuman capitalIncome economicsInflationLabor ProductivityLaborLabourMacroeconomicsNational Government Expenditures and Related Policies: InfrastructuresOccupational ChoiceOther Public Investment and Capital StockPrice LevelPricesPublic finance & taxationPublic FinancePublic investment and public-private partnerships (PPP)Public-private sector cooperationSkills332.015195Feldkircher Martin1815626Zeugner Stefan1815627International Monetary Fund.Finance Department.DcWaIMFBOOK9910961802503321Benchmark Priors Revisited4371089UNINA