Journal
AGRICULTURAL AND RESOURCE ECONOMICS REVIEW
Volume 47, Issue 1, Pages 178-194Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/age.2017.26
Keywords
aggregated data; agricultural census; Bayesian methods; count data; disaggregation; downscaling; farm counts; posterior distribution
Categories
Funding
- USDA National Institute of Food and Agriculture Hatch Projects [RI00H-108, 229284, RI0017-NC1177, 1011736]
- USDA Economic Research Service Cooperative Research Agreement [58-6000-5-0091]
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Policy-critical, micro-level statistical data are often unavailable at the desired level of disaggregation. We present a Bayesian methodology for downscaling aggregated count data to the micro level, using an outside statistical sample. Our procedure combines numerical simulation with exact calculation of combinatorial probabilities. We motivate our approach with an application estimating the number of farms in a region, using count totals at higher levels of aggregation. In a simulation analysis over varying population sizes, we demonstrate both robustness to sampling variability and outperformance relative to maximum likelihood. Spatial considerations, implementation of informative priors, non-spatial classification problems, and best practices are discussed.
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