IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v36y2020i6p1060-1079.html
   My bibliography  Save this article

Modeling swine population dynamics at a finer temporal resolution

Author

Listed:
  • Luca Sartore
  • Yijun Wei
  • Emilola Abayomi
  • Seth Riggins
  • Gavin Corral
  • Valbona Bejleri
  • Clifford Spiegelman

Abstract

The United States Department of Agriculture's National Agricultural Statistics Service (NASS) uses probability surveys of hog owners to estimate quarterly hog inventories in the United States at the national and state levels. NASS also receives data from external sources. A panel of commodity experts forms the Agricultural Statistics Board (ASB). The ASB establishes the NASS official estimates for each quarter by taking into account survey estimates and other relevant sources of information that are available in numerical and non‐numerical form. The aim of this article is to propose an estimation method of hog inventories by combining the NASS proprietary survey results, the hog transaction data, the past ASB panel expert analyses, biological dynamics, and the inter‐inventory relationship constraints. This approach downscales the official estimates to provide monthly estimates according to well‐defined biological growth patterns. The model developed in this study provides national estimates that may inform the quarterly reports.

Suggested Citation

  • Luca Sartore & Yijun Wei & Emilola Abayomi & Seth Riggins & Gavin Corral & Valbona Bejleri & Clifford Spiegelman, 2020. "Modeling swine population dynamics at a finer temporal resolution," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(6), pages 1060-1079, November.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:6:p:1060-1079
    DOI: 10.1002/asmb.2597
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2597
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2597?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Key, Nigel D. & McBride, William D., 2007. "The Changing Economics of U.S. Hog Production," Economic Research Report 6389, United States Department of Agriculture, Economic Research Service.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    2. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    3. Larue, Solène & Latruffe, Laure, 2009. "Agglomeration externalities and technical efficiency in French pig production," Working Papers 210403, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    4. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    5. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    6. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    7. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    9. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    10. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    11. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    12. Rivera, Nilza & Guzmán, Juan Ignacio & Jara, José Joaquín & Lagos, Gustavo, 2021. "Evaluation of econometric models of secondary refined copper supply," Resources Policy, Elsevier, vol. 73(C).
    13. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    14. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    16. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    17. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    18. repec:cup:judgdm:v:14:y:2019:i:4:p:395-411 is not listed on IDEAS
    19. I. Yu. Zolotova & V. V. Dvorkin, 2017. "Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks," Studies on Russian Economic Development, Springer, vol. 28(6), pages 608-615, November.
    20. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    21. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    22. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:apsmbi:v:36:y:2020:i:6:p:1060-1079. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.