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

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 insuranc...

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Detalles Bibliográficos
Autores principales: Woodard, J.D., Shee, Apurba, Mude, Andrew G.
Formato: Journal Article
Lenguaje:Inglés
Publicado: Springer 2016
Materias:
Acceso en línea:https://hdl.handle.net/10568/76923
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author Woodard, J.D.
Shee, Apurba
Mude, Andrew G.
author_browse Mude, Andrew G.
Shee, Apurba
Woodard, J.D.
author_facet Woodard, J.D.
Shee, Apurba
Mude, Andrew G.
author_sort Woodard, J.D.
collection Repository of Agricultural Research Outputs (CGSpace)
description 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.
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spelling CGSpace769232024-10-25T07:55:14Z A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data Woodard, J.D. Shee, Apurba Mude, Andrew G. insurance resilience drought pastoralism production livestock production spatial analysis livestock 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. 2016-04 2016-09-02T12:04:06Z 2016-09-02T12:04:06Z Journal Article https://hdl.handle.net/10568/76923 en Limited Access Springer Woodard, J., Shee, A. and Mude, A. 2016. A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data. Geneva Papers on Risk and Insurance – Issues and Practice, 41(2):259-279
spellingShingle insurance
resilience
drought
pastoralism
production
livestock production
spatial analysis
livestock
Woodard, J.D.
Shee, Apurba
Mude, Andrew G.
A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data
title A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data
title_full A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data
title_fullStr A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data
title_full_unstemmed A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data
title_short A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data
title_sort spatial econometric approach to designing and rating scalable index insurance in the presence of missing data
topic insurance
resilience
drought
pastoralism
production
livestock production
spatial analysis
livestock
url https://hdl.handle.net/10568/76923
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