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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Keywords

    Agricultural Finance; Production Economics;

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