Kernel density estimation based on grouped data : the case of poverty assessment /

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Bibliographic Details
Author / Creator:Minoiu, Camelia, 1977- author.
Imprint:Washington, D.C. : International Monetary Fund, African Dept., 2008.
©2008
Description:1 online resource (34 pages) : illustrations.
Language:English
Series:IMF working paper ; WP/08/183
IMF working paper ; WP/08/183.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12499553
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Other authors / contributors:Reddy, Sanjay, author.
International Monetary Fund. African Department.
Notes:"July 2008."
Includes bibliographical references (pages 21-25).
Print version record.
Summary: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.
Other form:Print version: Minoiu, Camelia, 1977- Kernel density estimation based on grouped data. Washington, D.C. : International Monetary Fund, African Dept., 2008