Degree Type

Thesis

Date of Award

2003

Degree Name

Master of Science

Department

Economics

First Advisor

C. Phillip Baumel

Abstract

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.

DOI

https://doi.org/10.31274/rtd-180813-7880

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Andrew Dean Miller

Language

en

OCLC Number

52606283

File Format

application/pdf

File Size

136 pages

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