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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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Washington, D.C. :
International Monetary Fund,
2008.
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| Colección: | IMF Working Papers ;
Working Paper no. 08/183. |
| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | 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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| Notas: | Available in PDF, ePUB, and Mobi formats on the Internet. |
| Descripción Física: | 1 online resource (34 pages) |
| Bibliografía: | Includes bibliographical references (pages 21-25). |
| ISBN: | 1451914946 9781451914948 |
| ISSN: | 2227-8885 ; |