We just averaged over two trillion cross-country growth regressions /

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Bibliographic Details
Author / Creator:Ley, Eduardo.
Imprint:[Washington, D.C.] : International Monetary Fund, Fiscal Affairs Department, ©1999.
Description:1 online resource (20 pages) : illustrations
Language:English
Series:IMF working paper ; WP/99/101
IMF working paper ; WP/99/101.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12496211
Hidden Bibliographic Details
Other authors / contributors:Steel, Mark F. J.
International Monetary Fund. Fiscal Affairs Department.
ISBN:1451898126
9781451898125
128160125X
9781281601254
Notes:Includes bibliographical references (pages 19-20).
Restrictions unspecified
Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2010.
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212
digitized 2010 HathiTrust Digital Library committed to preserve
Print version record.
Summary:We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian model averaging (BMA). We find that the posterior probability is distributed among many models, suggesting the superiority of BMA over any single model. Out-of-sample predictive results support that claim. In contrast with Levine and Renelt (1992), our results broadly support the more "optimistic" conclusion of Sala-i-Martin (1997b), namely, that some variables are important regressors for explaining cross-country growth patterns. However, the variables we identify as most useful for growth regression differ substantially from Sala-i-Martin's results
Other form:Print version: Ley, Eduardo. We just averaged over two trillion cross-country growth regressions. [Washington, D.C.] : International Monetary Fund, Fiscal Affairs Department, ©1999

MARC

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520 3 |a We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian model averaging (BMA). We find that the posterior probability is distributed among many models, suggesting the superiority of BMA over any single model. Out-of-sample predictive results support that claim. In contrast with Levine and Renelt (1992), our results broadly support the more "optimistic" conclusion of Sala-i-Martin (1997b), namely, that some variables are important regressors for explaining cross-country growth patterns. However, the variables we identify as most useful for growth regression differ substantially from Sala-i-Martin's results 
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