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020129s2008 dcu o i000 0 eng d |
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|a CEF
|b eng
|e pn
|c CEF
|d AU@
|d ZCU
|d OTZ
|d UWO
|d OCLCO
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|d OCLCQ
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|a 870245247
|a 1058172767
|a 1420103351
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|a 1451914946
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|a 9781451914948
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|z 9781451914948
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|a 10.5089/9781451914948.001
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|a DEBBG
|b BV044066424
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|a (OCoLC)883143559
|z (OCoLC)870245247
|z (OCoLC)1058172767
|z (OCoLC)1420103351
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|a dlr
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|a HC79.P6
|b .M56 2008
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|a 339.46
|2 23
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|a UAMI
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|a Minoiu, Camelia.
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|a Kernel Density Estimation Basedon Grouped Data :
|b the Case of Poverty Assessment /
|c Minoiu, Camelia.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2008.
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|a 1 online resource (34 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a IMF Working Papers,
|x 2227-8885 ;
|v Working Paper No. 08/183
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|a Available in PDF, ePUB, and Mobi formats on the Internet.
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|a We analyze the performance of kernel density methods applied to grouped data to estimate poverty (as applied in Sala-i-Martin, 2006, QJE). Using Monte Carlo simulations and household surveys, we find that the technique gives rise to biases in poverty estimates, the sign and magnitude of which vary with the bandwidth, the kernel, the number of datapoints, and across poverty lines. Depending on the chosen bandwidth, the $1/day poverty rate in 2000 varies by a factor of 1.8, while the $2/day headcount in 2000 varies by 287 million people. Our findings challenge the validity and robustness of poverty estimates derived through kernel density estimation on grouped data.
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|a Includes bibliographical references (pages 21-25).
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|3 Use copy
|f Restrictions unspecified
|2 star
|5 MiAaHDL
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|a Electronic reproduction.
|b [Place of publication not identified]:
|c HathiTrust Digital Library.
|d 2024.
|5 MiAaHDL
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|a 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.
|u http://purl.oclc.org/DLF/benchrepro0212
|5 MiAaHDL
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583 |
1 |
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|a digitized
|c 2024.
|h HathiTrust Digital Library
|l committed to preserve
|2 pda
|5 MiAaHDL
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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|a Poverty
|x Measurement.
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|a Income distribution
|x Econometric models.
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|a Kernel functions.
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4 |
|a Data Analysis.
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|a Economic Models.
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|a Grouped Data.
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|a Income Distribution.
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|a Kernel Density Estimation.
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|a Poverty.
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|a Revenu
|x Répartition
|x Modèles économétriques.
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|a Noyaux (Mathématiques)
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650 |
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7 |
|a Income distribution
|x Econometric models
|2 fast
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650 |
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7 |
|a Kernel functions
|2 fast
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650 |
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|a Poverty
|x Measurement
|2 fast
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|a Core.
|2 stw
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|a Einkommensverteilung.
|2 stw
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|a Armut.
|2 stw
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700 |
1 |
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|a Minoiu, Camelia.
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|a Reddy, Sanjay.
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|a International Monetary Fund.
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|i has work:
|a Kernel density estimation based on grouped data (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGVgx3jBCqCgpdbvjfh3ry
|4 https://id.oclc.org/worldcat/ontology/hasWork
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0 |
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|i Print Version:
|z 9781451914948
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830 |
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|a IMF Working Papers ;
|v Working Paper no. 08/183.
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856 |
4 |
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|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1607966
|z Texto completo
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938 |
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|a ProQuest Ebook Central
|b EBLB
|n EBL1607966
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|a YBP Library Services
|b YANK
|n 11617751
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