Constrained nonparametric estimation of input distance function

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

Abstract

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.

Publication DOI: https://doi.org/10.1007/s11123-013-0372-9
Divisions: Aston Business School > Economics, Finance & Entrepreneurship
Aston Business School
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s11123-013-0372-9
Uncontrolled Keywords: nonparametric estimation,input distance function,constraints,elasticities
Full Text Link: http://link.spr ... 1123-013-0372-9
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Published Date: 2015-02-01
Authors: Sun, Kai

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Version: Accepted Version


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