IDEAS home Printed from https://ideas.repec.org/p/ags/iaae12/126364.html
   My bibliography  Save this paper

Forecasting weekly Canary tomato exports from annual surface data

Author

Listed:
  • Martin-Rodriguez, Gloria
  • Caceres-Hernandez, Jose Juan

Abstract

Sea shipping is the main transport mode used by Canary farmers to export tomatoes to the European markets. Provincial associations of Canary growers negotiate charter fees with the shipping companies for the whole exporting period and, therefore, provide a unified sea transport service. When such a negotiation takes place each year, the individual growers’ decisions about planting surface are usually known. However, the forecasting of the distribution of tomato exports over the whole harvesting period would help Canary associations make more timely and effective decisions. In this paper, a model is proposed to forecast weekly Canary tomato exports conditioned on a given total planting surface. A seasonal model is formulated to deal with the weekly seasonal pattern of Canary tomato yields per hectare by means of evolving splines. Such a model is a useful tool to forecast weekly yields. From these forecasts, weekly tomato exports beyond the end of the sample are also forecast by taking the total planting surface into account. To illustrate the aptness of this framework, the proposed methodology is applied to a weekly series of tomatoes exported to the European markets from 1995/1996 to 2010/2011 harvests.

Suggested Citation

  • Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2012. "Forecasting weekly Canary tomato exports from annual surface data," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126364, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae12:126364
    DOI: 10.22004/ag.econ.126364
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/126364/files/Forecasting.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.126364?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. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
    2. Martín Rodríguez, Gloria & Cáceres Hernández, José Juan, 2010. "Splines and the proportion of the seasonal period as a season index," Economic Modelling, Elsevier, vol. 27(1), pages 83-88, January.
    3. Alberto Cabrero & Gonzalo Camba-Mendez & Astrid Hirsch & Fernando Nieto, 2009. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 194-217.
    4. Adusei Jumah & Robert M. Kunst, 2008. "Seasonal prediction of European cereal prices: good forecasts using bad models?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 391-406.
    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. Mariam El Hamiani Khatat, 2018. "Monetary Policy and Models of Currency Demand," IMF Working Papers 2018/028, International Monetary Fund.
    2. Kaushik Bhattacharya & Sunny Kumar Singh, 2016. "Impact of Payment Technology on Seasonality of Currency in Circulation: Evidence from the USA and India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 117-136, June.
    3. Marek Hlavacek & Michael Konak & Josef Cada, 2005. "The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation," Working Papers 2005/11, Czech National Bank.
    4. García, Juan R. & Pacce, Matías & Rodrigo, Tomasa & Ruiz de Aguirre, Pep & Ulloa, Camilo A., 2021. "Measuring and forecasting retail trade in real time using card transactional data," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1235-1246.
    5. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    6. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
    7. Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    8. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    9. Adusei Jumah & Robert M. Kunst, 2016. "Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging," Applied Economics, Taylor & Francis Journals, vol. 48(45), pages 4366-4378, September.
    10. Faruk Balli & Elsayed Mousa Elsamadisy, 2012. "Modelling the currency in circulation for the State of Qatar," International Journal of Islamic and Middle Eastern Finance and Management, Emerald Group Publishing Limited, vol. 5(4), pages 321-339, November.
    11. Rishab Guha & Serena Ng, 2019. "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 403-436, National Bureau of Economic Research, Inc.
    12. Deinhammer, Harald & Ladi, Anna, 2017. "Modelling euro banknote quality in circulation," Occasional Paper Series 204, European Central Bank.
    13. Diego Bodas & Juan Ramon Garcia & Juan Murillo & Matias Pacce & Tomasa Rodrigo & Juan de Dios Romero & Pep Ruiz & Camilo Ulloa & Heribert Valero, 2018. "Measuring Retail Trade Using Card Transactional Data," Working Papers 18/03, BBVA Bank, Economic Research Department.
    14. Martín Rodríguez, G. & Cáceres Hernández, J. J. & Guirao Pérez, G, 2005. "Un modelo para la exportación semanal de tomate de Almería/A model for the Weekly Tomato Exports from Almería," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 23, pages 731-751, Diciembre.
    15. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2009. "The Proportion of the Seasonal Period as a Season Index in Weekly Agricultural Data," 2009 Conference, August 16-22, 2009, Beijing, China 49956, International Association of Agricultural Economists.
    16. Alberto Cabrero & Gonzalo Camba-Mendez & Astrid Hirsch & Fernando Nieto, 2009. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 194-217.
    17. Ito, Ryoko, 2013. "Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
    18. Juan Sebastián Becerra C. & Luis Ceballos S. & Felipe Córdova F. & Michael Pedersen, 2010. "Market Interest Rate Dynamics in Times of Financial Turmoil," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(1), pages 5-22, April.
    19. Isengildina-Massa, Olga & MacDonald, Stephen, 2009. "U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49324, Agricultural and Applied Economics Association.
    20. Mr. Romain M Veyrune & Shaoyu Guo, 2019. "Autonomous Factor Forecast Quality: The Case of the Eurosystem," IMF Working Papers 2019/296, International Monetary Fund.

    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:iaae12:126364. 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/iaaeeea.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.