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.
|Schools:||Schools > Salford Business School > Business and Management Research Centre|
|Journal or Publication Title:||Journal of Productivity Analysis|
|Funders:||Non funded research|
|Depositing User:||Dr Kai Sun|
|Date Deposited:||29 May 2015 17:45|
|Last Modified:||24 Nov 2015 16:44|
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