IDEAS home Printed from https://ideas.repec.org/p/ags/aaea22/343687.html
   My bibliography  Save this paper

A Multi-Model, Ensemble Approach to Forecasting United States Food Prices

Author

Listed:
  • Liang, Weifang
  • Liu, Yong
  • Somogyi, Simon
  • Anderson, David P.

Abstract

No abstract is available for this item.

Suggested Citation

  • Liang, Weifang & Liu, Yong & Somogyi, Simon & Anderson, David P., 2024. "A Multi-Model, Ensemble Approach to Forecasting United States Food Prices," 2024 Annual Meeting, July 28-30, New Orleans, LA 343687, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea22:343687
    DOI: 10.22004/ag.econ.343687
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/343687/files/28290.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.343687?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. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. MacLachlan, Matthew & Chelius, Carolyn & Short, Gianna, 2022. "Time-Series Methods for Forecasting and Modeling Uncertainty in the Food Price Outlook," Amber Waves:The Economics of Food, Farming, Natural Resources, and Rural America, United States Department of Agriculture, Economic Research Service, vol. 2022(Technical), August.
    3. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    4. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    5. MacLachlan, Matthew & Chelius, Carolyn & Short, Gianna, 2022. "Time-Series Methods for Forecasting and Modeling Uncertainty in the Food Price Outlook," Amber Waves:The Economics of Food, Farming, Natural Resources, and Rural America, United States Department of Agriculture, Economic Research Service, vol. 2022(Technical), August.
    6. MacLachlan, Matthew & Chelius, Carolyn & Short, Gianna, 2022. "Time-Series Methods for Forecasting and Modeling Uncertainty in the Food Price Outlook," USDA Miscellaneous 327370, United States Department of Agriculture.
    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. McWilliams, William N. & Isengildina Massa, Olga & Stewart, Shamar L., 2024. "Annual Food Price Inflation Forecasting: A Macroeconomic Random Forest Approach," 2024 Annual Meeting, July 28-30, New Orleans, LA 343923, Agricultural and Applied Economics Association.
    2. Delgado, Michael S. & McCloud, Nadine & Kumbhakar, Subal C., 2014. "A generalized empirical model of corruption, foreign direct investment, and growth," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 298-316.
    3. Hans R. A. Koster & Jos N. van Ommeren & Piet Rietveld, 2016. "Historic amenities, income and sorting of households," Journal of Economic Geography, Oxford University Press, vol. 16(1), pages 203-236.
    4. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    5. Qi Li & Juan Lin & Jeffrey S. Racine, 2013. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 57-65, January.
    6. Denafas, Gintaras & Ruzgas, Tomas & Martuzevičius, Dainius & Shmarin, Sergey & Hoffmann, Michael & Mykhaylenko, Valeriy & Ogorodnik, Stanislav & Romanov, Mikhail & Neguliaeva, Ekaterina & Chusov, Alex, 2014. "Seasonal variation of municipal solid waste generation and composition in four East European cities," Resources, Conservation & Recycling, Elsevier, vol. 89(C), pages 22-30.
    7. Karimu, Amin & Brännlund, Runar & Lundgren, Tommy & Söderholm, Patrik, 2017. "Energy intensity and convergence in Swedish industry: A combined econometric and decomposition analysis," Energy Economics, Elsevier, vol. 62(C), pages 347-356.
    8. Bos, Martijn & Demirer, Riza & Gupta, Rangan & Tiwari, Aviral Kumar, 2018. "Oil returns and volatility: The role of mergers and acquisitions," Energy Economics, Elsevier, vol. 71(C), pages 62-69.
    9. Yanyao Yi & Ting Ye & Menggang Yu & Jun Shao, 2020. "Cox regression with survival‐time‐dependent missing covariate values," Biometrics, The International Biometric Society, vol. 76(2), pages 460-471, June.
    10. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    11. Bourdon, Jean & Frölich, Markus & Michaelowa, Katharina, 2007. "Teacher Shortages, Teacher Contracts and their Impact on Education in Africa," IZA Discussion Papers 2844, Institute of Labor Economics (IZA).
    12. Besstremyannaya, Galina, 2015. "Measuring the effect of health insurance companies on the quality of healthcare systems with kernel and parametric regressions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 3-20.
    13. E. Zacharias & T. Stengos, 2006. "Intertemporal pricing and price discrimination: a semiparametric hedonic analysis of the personal computer market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 371-386.
    14. Michael S. Delgado & Daniel J. Henderson & Christopher F. Parmeter, 2014. "Does Education Matter for Economic Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 334-359, June.
    15. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286.
    16. Frölich, Markus & Michaelowa, Katharina, 2004. "Peer effects and textbooks in primary education: Evidence from francophone sub-Saharan Africa," HWWA Discussion Papers 311, Hamburg Institute of International Economics (HWWA).
    17. Ramos, Francisco López & Batres, Rafael & De-la-Cruz-Márquez, Cynthia Griselle & Anzures, Melina López, 2023. "Optimization models for nopal crop planning with land usage expansion and government subsidy," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    18. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    19. Miller, Steve & Startz, Richard, 2019. "Feasible generalized least squares using support vector regression," Economics Letters, Elsevier, vol. 175(C), pages 28-31.
    20. Nolwenn Roudaut & Anne Vanhems, 2012. "Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach," Journal of Productivity Analysis, Springer, vol. 37(2), pages 155-169, April.

    More about this item

    Keywords

    Research Methods/Statistical Methods;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ags:aaea22:343687. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    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.