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7 Modeling Swine Population Dynamics
Pages 43-52

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From page 43...
... CURRENT MODELING INNOVATIONS Sartore explained that NASS is interested in a model that produces estimates at a finer temporal and spatial resolution than currently used models. In particular, although NASS publishes quarterly estimates, the new model, called Satorie, Wei, Abayomi, Riggins, Corral, Sendransk (SWARCS)
From page 44...
... The monthly adjustment ratios are set equal to the relevant quarterly ratio. This process results in estimated national totals.
From page 45...
... between monthly sows farrowed and the quarterly breeding herd inventory 2 months earlier plus an error term. Parameters are estimated by minimizing the sum of squared residuals plus a penalty function involving the product of a positive parameter "delta" and the sum of the absolute values of the AR and MA parameters.
From page 46...
... Estimation uses the Broyden-Fletcher-GoldfarbShanno iterative algorithm with initial survival rates set to 1, transition rates for the first two weight categories set to 0.25, and for the last two weight categories set to 0.75. Sartore said that the model is estimated in stages.
From page 47...
... He said that an issue remains with estimates for the breeding herd. In the future, he said, NASS would like to extend the model to provide state-level estimates; provide a time series model for survival rates instead of using a spline-based approach; account for data quality at the operation level; and incorporate webscraping information for disease outbreak detection.
From page 48...
... New information comes primarily from new slaughter data that are available regularly at a national level but are not yet available at the time the initial estimates are made. Katherine Ensor asked about any out-of-sample performance metrics for the model, such as where the model is trained on one part of data and evaluated on another.
From page 49...
... Slud said that the only way he knows to combine survey variances, such as those obtained from a bootstrap or jackknife, with model variances is to do parametric model bootstrap loops within survey-bootstrap loops. Sartore replied that it has to be done that way.
From page 50...
... Sartore replied that it was to account for the obvious annual seasonality. He observed that the KFM also uses first and seasonal differences, but for quarterly data.
From page 51...
... The field offices collect the data, prepare estimates, and send them to headquarters. Plain asked a final question about the comparison of RMSEs between the two models and initial estimates for all published variables.


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