This dissertation contains three chapters on kernel-based non/semiparametric models with applications in economics of technology. Input distance function is chosen as the primal representation of technology.
The first chapter discusses estimation of classical econometric models subject to arbitrary constraints, since estimation of economic relationships often requires imposition of economic regularity conditions. The method of constraint weighted bootstrapping is described to impose the constraints on a class of linear estimators, including parametric (e.g., ordinary least squares) and nonparametric (e.g., kernel) estimators. The method is implemented with a Norwegian dairy farm panel data set. Both unconstrained and constrained input distance functions are estimated with both parametric and nonparametric estimators.
The second chapter employs the constrained kernel-based method described in the first chapter to estimate Morishima elasticity of complementarity, derived from input distance function, between labor, material and capital for Norwegian timber producers. The three inputs are found pairwise net complements for most forest owners. Since private forest owners nowadays increasingly focus on recreational services which may affect employment in timber production, the elasticity estimates may have policy implications as to how the owners could potentially reallocate their inputs to benefit labor share.
While the first two chapters use purely nonparametric estimation method, the third chapter employs a semiparametric smooth coefficient model to estimate total factor productivity (TFP) growth and its components for the U.S. electricity industry. The semiparametric model is derived from a nonparametric specification of input distance function, using a growth formulation. Input and scale biases in technical change are also estimated in a fully flexible manner. Based on a comparison of parametric (i.e., translog) and semiparametric models against the Divisia TFP growth, the semiparametric model performs the best in tracking the temporal behavior of TFP growth.
|Advisor:||Henderson, Daniel J., Kumbhakar, Subal C.|
|Commitee:||Jones, Barry E., Schick, Anton|
|School:||State University of New York at Binghamton|
|School Location:||United States -- New York|
|Source:||DAI-A 72/12, Dissertation Abstracts International|
|Keywords:||Constrained kernel-based method, Input distance functions, Total factor productivity|
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