{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/16920"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/16920","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evaluating yield models for crop insurance rating","abstract":"Crop insurance performance and loss rates depend directly on underlying crop yield distributions. However, there still exists much debate about how to represent the underlying crop yield distributions. Using farm-level corn and soybean yields from 1972-2008, this study examines in-sample goodness-of-fit measures of both the whole distribution and the insurance tail to compare a set of flexible parametric, semi-parametric, and non-parametric distributions in a meaningful economic context. Simulations are then conducted to investigate the out-of-sample efficiency properties of several competing distributions. The results indicate that more parameterized distributional forms fit the data better in-sample, but are generally less efficient out-of-sample - and in some cases more biased - than more parsimonious forms which also fit the data adequately, such as the Weibull. The results highlight the relative advantages of alternative distributions, in terms of the bias-efficiency tradeoff in both in- and out-of-sample frameworks.","abstract_html":"Crop insurance performance and loss rates depend directly on underlying crop yield distributions. However, there still exists much debate about how to represent the underlying crop yield distributions. Using farm-level corn and soybean yields from 1972-2008, this study examines in-sample goodness-of-fit measures of both the whole distribution and the insurance tail to compare a set of flexible parametric, semi-parametric, and non-parametric distributions in a meaningful economic context. Simulations are then conducted to investigate the out-of-sample efficiency properties of several competing distributions. The results indicate that more parameterized distributional forms fit the data better in-sample, but are generally less efficient out-of-sample - and in some cases more biased - than more parsimonious forms which also fit the data adequately, such as the Weibull. The results highlight the relative advantages of alternative distributions, in terms of the bias-efficiency tradeoff in both in- and out-of-sample frameworks.","abstract_has_math":false,"creators":["Lanoue, Christopher"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agr & Consumer Economics","degree_department":null,"school":null,"contributors":["Sherrick, Bruce J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-08-20T18:01:56Z","date_published":"2010-08-20T18:01:56Z","updated_at":"2026-07-22T22:25:09Z","subjects":["Yield distributions","Crop Insurance","Weibull Distribution","Beta Distribution","Mixture Distribution","Burr XII Distribution","Out-of-Sample Efficiency","Goodness-of-Fit","Insurance Rating Efficiency"],"languages":["en"],"rights":["Copyright 2010 Christopher Lanoue"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/16920","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sherrick, Bruce J."]},{"key":"dc:creator","label":"Author","values":["Lanoue, Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2010-08-20T18:01:56Z","2010-08"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agr & Consumer Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Yield distributions","Crop Insurance","Weibull Distribution","Beta Distribution","Mixture Distribution","Burr XII Distribution","Out-of-Sample Efficiency","Goodness-of-Fit","Insurance Rating Efficiency"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2010 Christopher Lanoue"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/16920"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Crop insurance performance and loss rates depend directly on underlying crop yield distributions. 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