Author Archive: Daisuke Yagi

MATLAB code for Shape Constrained Kernel-weighted Least Squares (SCKLS)

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Adaptations done by
Andrew L. Johnson ajohnson@tamu.edu
Daisuke Yagi d.yagi@tamu.edu

Function to compute the SCKLS estimator of the response y on the data matrix X
Function NEEDS the quadprog function in Optimization Toolbox  in the computer

To run the example files, you NEED the MATLAB code for the Local Polynomial Estimator we developed.

y: the response variable — a column vector of length n
X: the data matrix of the predictors (of size n*d);

For more details, please refer to the following paper:
Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries
2018. Accepted, Journal of Business and Economic Statistics.

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MATLAB code for Local Polynomial Estimator

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Adaptations done by
Andrew L. Johnson ajohnson@tamu.edu
Daisuke Yagi d.yagi@tamu.edu

Function to compute the (unconstrained) local polynomial regression of the response y on the data matrix X
Function NEEDS the quadprog function in Optimization Toolbox  in the computer

y: the response variable — a column vector of length n
X: the data matrix of the predictors (of size n*d);

For more details about Local Polynomial, please refer to the following book:
Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries

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Iterative S-shape production function estimation

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A production function satisfying the Regular Ultra Passum (RUP) law is characterized by increasing returns to scale followed by decreasing returns to scale along any expansion path, which is referred to as S-shape function. Although there are existing nonparametric estimators imposing the RUP law, they impose additional strong assumptions such as: deterministic model, homotheticity or constant elasticity of scale. This paper proposes an iterative algorithm to estimate adaptively a function that satisfies the RUP law while relaxing these other assumptions.