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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Using Models to Estimate Hog and Pig Inventories: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25526.
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References

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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Using Models to Estimate Hog and Pig Inventories: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25526.
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Ghosh, M., Nangia, N., and Kim, D.H. (1996). Estimation of median income of four-person families: A Bayesian time series approach. Journal of the American Statistical Association, 91, 1423–1431.

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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Using Models to Estimate Hog and Pig Inventories: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25526.
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Page 85
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Using Models to Estimate Hog and Pig Inventories: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25526.
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In 2014, the National Agricultural Statistics Service (NASS) engaged the National Academies of Sciences, Engineering, and Medicine to convene a planning committee to organize a public workshop for an expert open discussion of their then-current livestock models. The models had worked well for some time. Unfortunately beginning in 2013, an epidemic that killed baby pigs broke out in the United States. The epidemic was not fully realized until 2014 and spread to many states. The result was a decline in hog inventories and pork production that was not predicted by the models. NASS delayed the workshop until 2019 while it worked to develop models that could help in times both of equilibrium and shock (disease or disaster), as well as alternative approaches to help detect the onset of a shock. The May 15, 2019, workshop was consistent with NASS’s 2014 intention, but with a focus on a model that can help predict hog inventories over time, including during times of shock. This publication summarizes the presentations and discussions from the workshop.

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