Bootstrap Algorithms for Risk Models with Auxiliary Variable and Complex Samples

Giancarlo Manzi and Fulvia Mecatti ()
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Fulvia Mecatti: University of Milan–Bicocca

Methodology and Computing in Applied Probability, 2009, vol. 11, issue 1, 21-27

Abstract: Abstract Resampling methods are often invoked in risk modelling when the stability of estimators of model parameters has to be assessed. The accuracy of variance estimates is crucial since the operational risk management affects strategies, decisions and policies. However, auxiliary variables and the complexity of the sampling design are seldom taken into proper account in variance estimation. In this paper bootstrap algorithms for finite population sampling are proposed in presence of an auxiliary variable and of complex samples. Results from a simulation study exploring the empirical performance of some bootstrap algorithms are presented.

Keywords: Probability-proportional-to-size sampling; Ratio model; Ratio estimator; Regression estimator; Resampling methods; 02.50.Tt; 02.70.Rr; 62F40 (search for similar items in EconPapers)
JEL-codes: C15 C42 C81 (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s11009-008-9072-8

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