Article
Duality theory in empirical work, revisited
European Review of Agricultural Economics
Document Type
Article
Disciplines
Publication Version
Accepted Manuscript
Publication Date
8-10-2017
Publisher
European Review of Agricultural Economics
DOI
10.1093/erae/jbx017
Abstract
We compute a pseudo-dataset by Monte Carlo simulations featuring important characteristics of US agriculture, such that the initial technology parameters are known, and employing widely used datasets for calibration. Then, we show the usefulness of this calibration by applying the duality theory approach to datasets bearing as sources of noise only the aggregation of technologically heterogeneous firms. Estimation recovers initial parameters with reasonable accuracy. These conclusions are expected, but the proposed calibration sets the basis for analysing the performance of duality theory in empirical work when datasets have more observed and unobserved sources of noise, as those faced by practitioners.
Copyright Owner
European Review of Agricultural Economics
Copyright Date
2017
Language
en
File Format
application/pdf
Citation Information
Francisco Rosas and Sergio H Lence. "Duality theory in empirical work, revisited" European Review of Agricultural Economics Vol. 44 Iss. 5 (2017) p. 836 - 859 Available at: http://works.bepress.com/sergio_lence/55/
This article is published as Rosas, Francisco, and Sergio H. Lence. "Duality theory in empirical work, revisited." European Review of Agricultural Economics (2017): 1-24. doi: https://doi.org/10.1093/erae/jbx017. Posted with permission.