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a generalized maximum entropy stochastic frontier measuring productivity accounting for spatial dependency广义最大熵随机前沿衡量生产力占空间依赖性.pdf

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a generalized maximum entropy stochastic frontier measuring productivity accounting for spatial dependency广义最大熵随机前沿衡量生产力占空间依赖性

Entropy 2011, 13, 1916-1927; doi:10.3390/ OPEN ACCESS entropy ISSN 1099-4300 /journal/entropy Article A Generalized Maximum Entropy Stochastic Frontier Measuring Productivity Accounting for Spatial Dependency Axel Tonini 1,* and Valerien Pede 2 1 LEI, Part of Wageningen UR, PO Box 29703, The Hague 2585 DB, The Netherlands 2 International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, The Philippines; E-Mail: v.pede@ * Author to whom correspondence should be addressed: E-Mail: axel.tonini@wur.nl; Tel.: +31-0-70-3358322; Fax: +31-0-70-3615624. Received: 29 September 2011; in revised form: 17 October 2011 / Accepted: 24 October 2011 / Published: 28 October 2011 Abstract: In this paper, a stochastic frontier model accounting for spatial dependency is developed using generalized maximum entropy estimation. An application is made for measuring total factor productivity in European agriculture. The empirical results show that agricultural productivity growth in Europe is driven by upward movements of technology over time through technological developments. Results are then compared for a situation in which spatial dependency in the technical inefficiency effects is not accounted. Keywords: stochastic frontier; spatial dependency; generalized maximum entropy PACS Codes: 89.65.Gh; 89.70.Cf 1. Introduction This paper develops a stochastic frontier model accounting for spatial dependency through spatial econometrics techniques and using generalized maximum entropy (GME) estimation. An ap