English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51771/86989 (60%)
Visitors : 8370597      Online Users : 57
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/24783


    Title: Semiparametric Bayesian inference of the Kuznets hypothesis
    Authors: Huang, Ho-chuan;Lin, Shu-Chin
    Contributors: 淡江大學經濟學系
    Keywords: Kuznets hypothesis;Inverted-U;Inverted-√;Asymmetric;Bayesian
    Date: 2007-07-01
    Issue Date: 2009-11-30 18:36:53 (UTC+8)
    Publisher: Elsevier
    Abstract: In contrast to the parametric (quadratic) specification used for examining the Kuznets hypothesis, this study relies on the semiparametric Bayesian inference of the partially linear regression to re-assess the validity of an inverted-U shape of the Kuznets curve. The simple framework permits us to perform estimation and model comparison in a unified way. Empirical results using cross-sectional data on 75 countries indicate that there exists an (approximately) inverted-√, hence, asymmetric relation between inequality and per capita GDP. Moreover, judged by the Bayes factor, overwhelming evidence is found in favor of our semiparametric specification. Finally, robustness check using alternative measure of inequality and pool data confirms our findings.
    Relation: Journal of Development Economics 83(2), pp.491-505
    DOI: 10.1016/j.jdeveco.2006.03.008
    Appears in Collections:[財務金融學系暨研究所] 期刊論文
    [經濟學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    0KbUnknown288View/Open
    index.html0KbHTML62View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback