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Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches

Author

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  • Louhichi, Kamel
  • Jacquet, Florence
  • Butault, Jean Pierre
Abstract
This paper proposes the use of the Generalized Maximum Entropy (GME) method to estimate input allocation in multi-crop systems using heterogeneous data sources (farm accountancy data and cropping practices survey data). The aim is to explore the role of well-defined a priori information in improving the accuracy of GME estimation. The performance of the GME method is compared afterward to a Bayesian approach— Highest Posterior Density (HPD)—to assess their accuracy when reliable non-sample (prior) information is used and investigate their usefulness for reconciling heterogeneous data sources. Both approaches are applied to a given set of farm accounting data which reports information on input allocation between alternative input uses. The estimation results show that the use of well-defined prior information from external data source improves GME estimates even though this performance is not always significant. It also appears that the Bayesian (HPD) approach could be a good alternative to the GME estimator. HPD provides results that are close to the GME method with the advantage of a straightforward and transparent implementation of the a priori information.

Suggested Citation

  • Louhichi, Kamel & Jacquet, Florence & Butault, Jean Pierre, 2012. "Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 13(2), pages 1-20.
    • Handle: RePEc:ags:aergaa:253511
    DOI: 10.22004/ag.econ.253511
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    2. Rick Cox & Shalika Walker & Joep van der Velden & Phuong Nguyen & Wim Zeiler, 2020. "Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids," Energies, MDPI, vol. 13(9), pages 1-27, May.
    3. António Xavier & Maria Belem Freitas & Maria do Socorro Rosário & Rui Fragoso, 2016. "Disaggregating Statistical Data at Field Level: An Entropy Approach," CEFAGE-UE Working Papers 2016_06, University of Evora, CEFAGE-UE (Portugal).
    4. António Xavier & Rui Fragoso & Maria De Belém Costa Freitas & Maria Do Socorro Rosário & Florentino Valente, 2018. "A Minimum Cross-Entropy Approach to Disaggregate Agricultural Data at the Field Level," Land, MDPI, vol. 7(2), pages 1-16, May.

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