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A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data

A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data

Woodard, Joshua D., Shee, Apurba ORCID: 0000-0002-1836-9637 and Mude, Andrew (2016) A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data. The Geneva Papers on Risk and Insurance - Issues and Practice, 41 (2). pp. 259-279. ISSN 1018-5895 (Print), 1468-0440 (Online) (doi:https://doi.org/10.1057/gpp.2015.31)

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Abstract

Index-Based Livestock Insurance has emerged as a promising market-based solution for insuring livestock against drought-related mortality. The objective of this work is to develop an explicit spatial econometric framework to estimate insurable indexes that can be integrated within a general insurance pricing framework. We explore the problem of estimating spatial panel models when there are missing dependent variable observations and cross-sectional dependence, and implement an estimable procedure which employs an iterative method. We also develop an out-of-sample efficient cross-validation mixing method to optimise the degree of index aggregation in the context of spatial index models.

Item Type: Article
Uncontrolled Keywords: Index insurance; Spatial econometric models with missing data; NDVI; Kenya pastoralist livestock production; Cross-validation; Model mixing
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Development Studies Research Group
Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department
Last Modified: 17 Dec 2018 14:28
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT b
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/17673

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