The effects of data aggregation on econometric estimates of climate change impact on corn and soybean production in the Midwest
Park, Wayne Ivan
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https://hdl.handle.net/2142/21796
Description
Title
The effects of data aggregation on econometric estimates of climate change impact on corn and soybean production in the Midwest
Author(s)
Park, Wayne Ivan
Issue Date
1992
Doctoral Committee Chair(s)
Garcia, Philip
Department of Study
Agricultural and Biological Engineering
Discipline
Agricultural Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Agriculture, Agronomy
Agriculture, General
Economics, Agricultural
Physics, Atmospheric Science
Language
eng
Abstract
Climatologists have attempted to predict changes in regional climate patterns caused by increasing levels of CO$\sb2$ and other trace gases in the atmosphere. Although the effects of climate are felt at the individual firm level, aggregate effects on crop production will determine if significant price movements will occur. Therefore, aggregate economic analysis which is based on theory of the firm under uncertainty is needed to assess the impact of climate change on crop production. Econometric estimation of the relationship between yields and key climatic variables can provide estimates of impact of climate change on yields. Then, acreage response equations are a reasonable alternative for modeling adjustments in regional land allocation in response to changing yield expectations.
If estimates of climate change impact on regional and national crop production are desired, it becomes necessary to model acreage response to an aggregate level to make the problem manageable. Estimates can be made at crop reporting district (CRD), state, or higher levels of aggregation. This dissertation examines the problems of aggregation in econometric modeling, and attempts to determine the relative merits of CRD and state level models for assessing the economic impact of a climate change on midwestern corn and soybean production.
Aggregate equations, such as state level acreage or yield equations, will usually suffer from aggregation error, however, aggregation may also reduce problems with specification errors. Thus, tests for consistent aggregation and model selection criteria are presented for assessing the tradeoff between aggregation and specification errors. This tradeoff generally favored CRD level yield equations, but favored state level acreage equations due to greater specification problems of the disaggregate acreage equations. Nonetheless, the effect of aggregation on estimation of climate change impact was small relative to other sources of error. The results suggest that the aggregate effects of climate change in major corn-producing states may be estimated with state level data.
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