 Bayesian Cross-Sectional Analysis of the Conditional Distribution of Earnings of Men in the United States, 1967-1996: AppendicesJohn Geweke and Michael Keane () MPRA Paper from University Library of Munich, Germany Abstract: This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full conditional distribution. It applies these methods to the relationship of earnings of men in the United States to their age and education over the period 1967 through 1996. Principal findings include increasing returns to both education and experience over this period, rising variance of earnings conditional on age and education, a negatively skewed and leptokurtic conditional distribution of log earnings, and steadily increasing inequality with asymmetric and changing impacts on high- and low-wage earners. These results are insensitive to several alternative nonparametric specifications of the distribution of earnings conditional on age and education. Keywords: Bayesian nonparametric inference; smoothness priors; Wiener process; mixture of normals; smoothly mixing regressions (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:54286 Access Statistics for this paper More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC. |
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