Second moment characterization of agricultural export and income projection models

dc.contributor.advisor C. Phillip Baumel
dc.contributor.author Miller, Andrew
dc.contributor.department Economics
dc.date 2018-08-22T19:49:41.000
dc.date.accessioned 2020-06-30T07:58:20Z
dc.date.available 2020-06-30T07:58:20Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2003
dc.date.issued 2003-01-01
dc.description.abstract <p>Agricultural outlook baseline projections published by FAPRI, USDA and the OECD are intended as policy analysis tools. It is shown that projections have been used as forecasts. Therefore, users of baseline projections need information about the past performance of baseline projections. This thesis measures the projection errors of baseline projections using RMSE and MAPE and assesses the ability of models to predict directional movements. The baseline model results are compared to corresponding results of naive models. For projection errors, a mixed model is used to estimate the mean bias and confidence intervals about the mean. Projections of corn, soybean, wheat, beef and pork exports, corn, soybean and wheat farm prices and direct government farm payments are analyzed. Results from the baseline models are compared to the corresponding results from naive models. This analysis shows that baseline projections are not consistently superior to naive models in projecting the future export and price levels. For soybean, beef and pork exports and for corn, soybean and wheat prices, baseline projections have been shown to perform relatively well. However, baseline projections for corn and wheat exports and government payments models have relatively large RMSE and bias. It is important for the users of baseline projections to understand the assumptions and structure of the models. Projection users and funding agencies need to be aware of the magnitude and direction of projection errors and bias. Modelers need to update and refine models based on the results of past projection analyses to produce better projections given the baseline assumptions. Additionally, organizations should more strongly explain the proper use and limitations of baseline projections.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/17193/
dc.identifier.articleid 18192
dc.identifier.contextkey 8117530
dc.identifier.doi https://doi.org/10.31274/rtd-180813-7880
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/17193
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/71015
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/17193/ISU_1461363.pdf|||Fri Jan 14 21:18:10 UTC 2022
dc.subject.disciplines Agricultural and Resource Economics
dc.subject.disciplines Agricultural Economics
dc.subject.disciplines Economics
dc.subject.keywords Agricultural Economics
dc.subject.keywords Economics
dc.subject.keywords Agriculture--Economic aspects
dc.title Second moment characterization of agricultural export and income projection models
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 4c5aa914-a84a-4951-ab5f-3f60f4b65b3d
thesis.degree.level thesis
thesis.degree.name Master of Science
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