Methods for variance estimation under random hot deck imputation in business surveys

Paolo Righi (), Stefano Falorsi () and Andrea Fasulo ()
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Paolo Righi: Italian National Institute of Statistics
Stefano Falorsi: Italian National Institute of Statistics
Andrea Fasulo: Italian National Institute of Statistics

Rivista di statistica ufficiale, 2014, vol. 16, issue 1-2, 45-64

Abstract: When the imputed values are treated as if they were observed the precision of the estimates is generally overstated. In the paper three variance methods under imputatation are taken into account. Two of them are the well known bootstrap and Multiple Imputation. The third is a new method based on grouped jackknife easy to implement, not computer intensive and suitable when random hot deck imputation is performed. A simulative comparison on real business data has been carried out. The findings show that the proposed method has good performances with respect to the other ones.

Keywords: Bootstrap; Multiple Imputation; Jacknife; Extended DAGJK; Replicate weights; Monte Carlo simulation. (search for similar items in EconPapers)
JEL-codes: C15 C83 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:isa:journl:v:16:y:2011:i:1-2:p:45-64

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