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., . "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.
    • 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. Allen, P. Geoffrey, 1994. "Economic forecasting in agriculture," International Journal of Forecasting, Elsevier, vol. 10(1), pages 81-135, June.
    3. 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.
    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. Carl R. Zulauf & Scott H. Irwin, 1998. "Market Efficiency and Marketing to Enhance Income of Crop Producers," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 20(2), pages 308-331.
    3. 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.
    4. Matteo Richiardi, 2018. "Editorial," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 1-3.
    5. 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).
    6. Frederik Priem & Philip Stessens & Frank Canters, 2020. "Microsimulation of Residential Activity for Alternative Urban Development Scenarios: A Case Study on Brussels and Flemish Brabant," Sustainability, MDPI, vol. 12(6), pages 1-28, March.
    7. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    8. 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).
    9. 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.
    10. Álvaro Forteza Galceran, 2023. "The Distribution and Redistribution of Income in the Presence of Pensions," Documentos de Trabajo (working papers) 0323, Department of Economics - dECON.
    11. Green, Kesten C. & Armstrong, J. Scott, 2007. "Structured analogies for forecasting," International Journal of Forecasting, Elsevier, vol. 23(3), pages 365-376.
    12. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    13. Ma, Yong & Zhou, Mingtao & Li, Shuaibing, 2024. "Weathering market swings: Does climate risk matter for agricultural commodity price predictability?," Journal of Commodity Markets, Elsevier, vol. 36(C).
    14. 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.
    15. Voss, Kevin E., 2011. "Voss wins the Presidency! A commentary essay on "Predicting elections from biographical information about candidates: A test of the index method"," Journal of Business Research, Elsevier, vol. 64(4), pages 345-347, April.
    16. David N Wear & Jeffrey P Prestemon, 2019. "Spatiotemporal downscaling of global population and income scenarios for the United States," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-19, July.
    17. Tsionas, Mike G., 2022. "Random and Markov switching exponential smoothing models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    18. Concepció Patxot & Meritxell Solé & Guadalupe Souto & Martin Spielauer, 2018. "The Impact of the Retirement Decision and Demographics on Pension Sustainability: A Dynamic Microsimulation Analysis," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 84-108.
    19. Rolf Aaberge & Ugo Colombino, 2018. "Structural Labour Supply Models and Microsimulation," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 162-197.
    20. 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.

    More about this item

    Keywords

    ;
    ;

    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.