Innis Lecture: Inference on income distributions.
This paper attempts to provide a synthetic view of varied techniques available for performing inference on income distributions. Two main approaches can be distinguished: one in which the object of interest is some index of income inequality or poverty, the other based on notions of stochastic domin...
| Publicado en: | Canadian Journal of Economics Vol. 43; no. 4; pp. 1122 - 1149 |
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| Formato: | Artículo |
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Wiley-Blackwell
November 2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511537636&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511537636 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00084085 CJE jtl: Canadian Journal of Economics issn: 00084085 maglogo: N pubinfo: dt: November 2010 vid: 43 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 511537636 ppf: 1122 ppct: 27 formats: tig: atl: Innis Lecture: Inference on income distributions. aug: au: Davidson, Russell su: Probability theory Statistical bootstrapping Mathematical models of income distribution sug: subj: Probability theory Statistical bootstrapping Mathematical models of income distribution ab: This paper attempts to provide a synthetic view of varied techniques available for performing inference on income distributions. Two main approaches can be distinguished: one in which the object of interest is some index of income inequality or poverty, the other based on notions of stochastic dominance. From the statistical point of view, many techniques are common to both approaches, although of course some are specific to one of them. I assume throughout that inference about population quantities is to be based on a sample or samples, and, formally, all randomness is due to that of the sampling process. Inference can be either asymptotic or bootstrap based. In principle, the bootstrap is an ideal tool, since in this paper I ignore issues of complex sampling schemes and suppose that observations are IID. However, both bootstrap inference and, to a considerably greater extent, asymptotic inference can fall foul of difficulties associated with the heavy right-hand tails observed with many income distributions. I mention some recent attempts to circumvent these difficulties. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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