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Kernel Density Estimation Basedon Grouped Data : the Case of Poverty Assessment /

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 w...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Minoiu, Camelia
Autor Corporativo: International Monetary Fund
Otros Autores: Reddy, Sanjay
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Washington, D.C. : International Monetary Fund, 2008.
Colección:IMF Working Papers ; Working Paper no. 08/183.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Minoiu, Camelia. 
245 1 0 |a Kernel Density Estimation Basedon Grouped Data :  |b the Case of Poverty Assessment /  |c Minoiu, Camelia. 
260 |a Washington, D.C. :  |b International Monetary Fund,  |c 2008. 
300 |a 1 online resource (34 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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490 1 |a IMF Working Papers,  |x 2227-8885 ;  |v Working Paper No. 08/183 
500 |a Available in PDF, ePUB, and Mobi formats on the Internet. 
520 3 |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. 
504 |a Includes bibliographical references (pages 21-25). 
506 |3 Use copy  |f Restrictions unspecified  |2 star  |5 MiAaHDL 
533 |a Electronic reproduction.  |b [Place of publication not identified]:  |c HathiTrust Digital Library.  |d 2024.  |5 MiAaHDL 
538 |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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590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Poverty  |x Measurement. 
650 0 |a Income distribution  |x Econometric models. 
650 0 |a Kernel functions. 
650 4 |a Data Analysis. 
650 4 |a Economic Models. 
650 4 |a Grouped Data. 
650 4 |a Income Distribution. 
650 4 |a Kernel Density Estimation. 
650 4 |a Poverty. 
650 6 |a Revenu  |x Répartition  |x Modèles économétriques. 
650 6 |a Noyaux (Mathématiques) 
650 7 |a Income distribution  |x Econometric models  |2 fast 
650 7 |a Kernel functions  |2 fast 
650 7 |a Poverty  |x Measurement  |2 fast 
650 7 |a Core.  |2 stw 
650 7 |a Einkommensverteilung.  |2 stw 
650 7 |a Armut.  |2 stw 
700 1 |a Minoiu, Camelia. 
700 1 |a Reddy, Sanjay. 
710 2 |a International Monetary Fund. 
758 |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 
776 0 8 |i Print Version:  |z 9781451914948 
830 0 |a IMF Working Papers ;  |v Working Paper no. 08/183. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1607966  |z Texto completo 
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