Campus Units

Economics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

8-10-2017

Journal or Book Title

European Review of Agricultural Economics

Volume

44

Issue

5

Publisher

European Review of Agricultural Economics

First Page or Article ID Number

836

Last Page

859

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.

JEL Classification

C15, D22, Q11

Comments

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.

Copyright Owner

European Review of Agricultural Economics

Language

en

File Format

application/pdf

Available for download on Saturday, August 10, 2019

Published Version

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