Bayesian Downscaling Methods for Aggregated Count Data. (17th December 2017)

• Record Type: Journal Article
• Authors: Michaud, Clayton P.; Sproul, Thomas W.
• Is Part Of: Agricultural and resource economics review. Volume 47:Number 1(2018)
• Page Start: 178
• Page End: 194
• Publication Date: 2017-12-17
• Subjects: aggregated data, -- agricultural census, -- Bayesian methods, -- count data, -- disaggregation, -- downscaling, -- farm counts, -- posterior distribution; C43, -- C11, -- Q12; Sustainable agriculture -- Periodicals; Sustainable agriculture -- United States -- Periodicals; Natural resources -- Periodicals; Agriculture -- Economic aspects -- Periodicals; Rural development -- Periodicals; 338.10974
• DOI: 10.1017/age.2017.26
• Languages: English
• ISSNs: 1068-2805

Abstract

Abstract : 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.