Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches
| dc.creator | Louhichi, Kamel | |
| dc.creator | Jacquet, Florence | |
| dc.creator | Butault, Jean Pierre | |
| dc.date | 2017-04-01T13:43:17Z | |
| dc.date.accessioned | 2026-07-09T11:11:43Z | |
| dc.description | 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. | |
| dc.identifier | doi:10.22004/ag.econ.253511 | |
| dc.identifier | https://ageconsearch.umn.edu/record/253511/files/13_2_6.pdf | |
| dc.identifier | http://ageconsearch.umn.edu/record/253511 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/625520 | |
| dc.language | eng | |
| dc.publisher | ||
| dc.source | http://ageconsearch.umn.edu/record/253511 | |
| dc.title | Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches | |
| dc.type | Text |