IDEAS home Printed from https://ideas.repec.org/a/ags/aolpei/188733.html
   My bibliography  Save this article

Microsimulation Model Estimating Czech Farm Income from Farm Accountancy Data Network Database

Author

Listed:
  • Hloušková, Z.
  • Lekešová, M.
  • Slížka, E.

Abstract

Agricultural income is one of the most important measures of economic status of agricultural farms and the whole agricultural sector. This work is focused on finding the optimal method of estimating national agricultural income from micro-economic database managed by the Farm Accountancy Data Network (FADN). Use of FADN data base is relevant due to the representativeness of the results for the whole country and the opportunity to carry out micro-level analysis. The main motivation for this study was a first forecast of national agricultural income from FADN data undertaken 9 months before the final official FADN results were published. Our own method of estimating the income estimation and the simulation procedure were established and successfully tested on the whole database on data from two preceding years. Present paper also provides information on used method of agricultural income prediction and on tests of its suitability.

Suggested Citation

  • Hloušková, Z. & Lekešová, M. & Slížka, E., 2014. "Microsimulation Model Estimating Czech Farm Income from Farm Accountancy Data Network Database," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(3), pages 1-11, September.
    • Handle: RePEc:ags:aolpei:188733
    DOI: 10.22004/ag.econ.188733
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/188733/files/agris_on-line_2014_3_hlouskova_lekesova_slizka.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.188733?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. Moon, Wanki, 2011. "Is agriculture compatible with free trade?," Ecological Economics, Elsevier, vol. 71(C), pages 13-24.
    2. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    3. Allen, P. Geoffrey, 1994. "Economic forecasting in agriculture," International Journal of Forecasting, Elsevier, vol. 10(1), pages 81-135, June.
    4. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    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. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    3. Giordana, Gastón A. & Pi Alperin, María Noel, 2023. "Old age takes its toll: Long-run projections of health-related public expenditure in Luxembourg," Economics & Human Biology, Elsevier, vol. 50(C).
    4. Holger Bonin & Karsten Reuss & Holger Stichnoth, 2015. "Life-Cycle Incidence of Family Policy Measures in Germany: Evidence from a Dynamic Microsimulation Model," SOEPpapers on Multidisciplinary Panel Data Research 770, DIW Berlin, The German Socio-Economic Panel (SOEP).
    5. Covey, Theodore & Erickson, Kenneth W., 2003. "Evaluating USDA Forecasts of Farm Assets: 1986-2002," 2003 Regional Committee NCT-194, October 6-7, 2003; Kansas City, Missouri 132405, Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition.
    6. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    7. Amare Tesfaw & Feyera Senbeta & Dawit Alemu & Ermias Teferi, 2021. "Value Chain Analysis of Eucalyptus Wood Products in the Blue Nile Highlands of Northwestern Ethiopia," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    8. Garcia, Philip & Irwin, Scott H. & Leuthold, Raymond M. & Yang, Li, 1997. "The value of public information in commodity futures markets," Journal of Economic Behavior & Organization, Elsevier, vol. 32(4), pages 559-570, April.
    9. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    10. Rolf Aaberge & Ugo Colombino, 2014. "Labour Supply Models," Contributions to Economic Analysis, in: Handbook of Microsimulation Modelling, volume 127, pages 167-221, Emerald Group Publishing Limited.
    11. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    12. GENEVOIS Anne-Sophie & LIEGEOIS Philippe & PI ALPERIN Maria Noel, 2019. "DyMH_LU: a simple tool for modelling and simulating the health status of the Luxembourgish elderly in the longer run," LISER Working Paper Series 2019-06, Luxembourg Institute of Socio-Economic Research (LISER).
    13. Li, Anzhi & Dorfman, Jeffrey H., 2014. "Composite Qualitative Forecasting of Futures Prices: Using One Commodity to Help Forecast Another," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169790, Agricultural and Applied Economics Association.
    14. Zegar, Józef S., 2012. "Gospodarstwa Rodzinne Wobec Wyzwań Wyżywienia I Ochrony Środowiska – Ujęcie Globalne," Village and Agriculture (Wieś i Rolnictwo), Polish Academy of Sciences (IRWiR PAN), Institute of Rural and Agricultural Development, vol. 4(157).
    15. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.
    16. Holger Bonin & Karsten Reuss & Holger Stichnoth, 2016. "The Monetary Value of Family Policy Measures in Germany over the Life Cycle: Evidence from a Dynamic Microsimulation Model," CESifo Economic Studies, CESifo Group, vol. 62(4), pages 650-671.
    17. Boumezoued, Alexandre & Hardy, Héloïse Labit & El Karoui, Nicole & Arnold, Séverine, 2018. "Cause-of-death mortality: What can be learned from population dynamics?," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 301-315.
    18. van de Ven, Justin, 2017. "SIDD: An adaptable framework for analysing the distributional implications of policy alternatives where savings and employment decisions matter," Economic Modelling, Elsevier, vol. 63(C), pages 161-174.
    19. Sebastian Dräger & Johannes Kopp & Ralf Münnich & Simon Schmaus, 2022. "Die zukünftige Entwicklung der Grundschulversorgung im Kontext ausgewählter Wanderungsszenarien [The future development of primary school demand in the context of selected migration scenarios]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(1), pages 51-77, March.
    20. Cameron, Andrew & Nelson, Rohan, 2022. "Enabling Users to Evaluate the Accuracy of ABARES Agricultural Forecasts," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 30(7), November.

    More about this item

    Keywords

    Agricultural Finance; Production Economics;

    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:aolpei:188733. 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/fevszcz.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.