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The Use of Maximum Entropy to Estimate Input‐Output Coefficients From Regional Farm Accounting Data
Author(s) -
Léony Yves,
Peeters Ludo,
Quinqu Maurice,
Surry Yves
Publication year - 1999
Publication title -
journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.157
H-Index - 61
eISSN - 1477-9552
pISSN - 0021-857X
DOI - 10.1111/j.1477-9552.1999.tb00891.x
Subject(s) - ordinary least squares , econometrics , estimation , principle of maximum entropy , mathematics , bayesian information criterion , statistics , sample (material) , entropy (arrow of time) , computer science , mathematical optimization , economics , physics , management , chromatography , quantum mechanics , chemistry
This paper proposes the use of the Generalised Maximum Entropy (GME) method to estimate input‐output coefficients, which reflect the unobserved allocation of farm input accounting costs to the various outputs produced. The GME method uses Shannon's information criterion as a basis for estimation. The performance of the GME method is compared with three other estimation techniques: Ordinary Least Squares (OLS), Bayesian estimation, and Linear Programming (LP). The various methods are applied to accounting data from a sample of beef‐dairy farms in Brittany, France. The analysis shows that the GME method offers an interesting alternative to “traditional” estimation methods. In contrast with the latter, though, the GME method is suitable to handle easily the problems of singularity, constrained estimation, and zero‐observations. Moreover, due to its flexibility, transparency and relative ease of implementation, the GME method is of great value to practitioners. However, the sensitivity of the GME estimates with respect to the design of the prior information set needs to be investigated further.