Constrained nonparametric estimation of input distance function

Sun, K 2015, 'Constrained nonparametric estimation of input distance function' , Journal of Productivity Analysis, 43 (1) , pp. 85-97.

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This paper proposes a constrained nonparametric method of estimating an input distance function. A regression function is estimated via kernel methods without functional form assumptions. To guarantee that the estimated input distance function satisfies its properties, monotonicity constraints are imposed on the regression surface via the constraint weighted bootstrapping method borrowed from statistics literature. The first, second, and cross partial analytical derivatives of the estimated input distance function are derived, and thus the elasticities measuring input substitutability can be computed from them. The method is then applied to a cross-section of 3,249 Norwegian timber producers.

Item Type: Article
Schools: Schools > Salford Business School > Salford Business School Research Centre
Journal or Publication Title: Journal of Productivity Analysis
Publisher: Springer
Refereed: Yes
ISSN: 0895-562X
Related URLs:
Funders: Non funded research
Depositing User: Dr Kai Sun
Date Deposited: 29 May 2015 17:45
Last Modified: 15 Feb 2022 19:09

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