Plenary Talk at European Workshop on Efficiency and Productivity Analysis (EWEPA) XIV

EWEPA

The largest conference in the field of efficiency and productivity analysis is the European Workshop on Efficiency and Productivity Analysis (EWEPA) XIV. This conference is held in odd years and the North American Productivity Workshop (NAPW) is held in even years. This years conference was help from June 16-18 in Helsinki, Finland. I was invited to give a plenary talk with Chris Parmeter. The topic was  Nonparametric smoothing and shape constraints.

Slides

“Effective Production: Measuring of the Sales Effect using Data Envelopment Analysis” accepted for publication at Annals of Operations Research

Effectiveness

Chia-Yen Lee (2012 graduate of the lab) published the fifth paper from his dissertation. In a previous paper, Lee and Johnson (2014), we define effectiveness as a way to distinguish between production performance and sales performance. In this paper we extend the concept to a panel data setting and analyze how efficiency and effectiveness evolve.

Abstract

Sales fluctuations lead to variations in the output levels affecting technical efficiency measures of operations when units sold are used at an output measure. The present study uses the concept of “effective production” and “effectiveness” to account for the effect of sales on operational performance measurements in a production system. The effectiveness measure complements the efficiency measure which does not account for the sales effect. The Malmquist productivity index is used to measure the sales effects characterized as the difference between the production function associated with efficiency and the sales-truncated production function associated with effectiveness. The proposed profit effectiveness is distinct from profit efficiency in that it accounts for sales. An empirical study of US airlines demonstrates the proposed method which describes the strategic position of a firm and a productivity-change analysis. These results demonstrate the concept of effectiveness and quantifies the effect of using sales as output.

EWEPA: Shape Constrained Nonparametric Estimation of Production Functions

EWEPA

This presentation was given at the European Workshop for Efficiency and Productivity Analysis (EWEPA XIV) as the plenary talk, June 16, 2015 in Helsinki Finland. I focus on the benefits of shape constraints to improve the finite sample performance of nonparametric estimators. Existing estimators and new estimators are presented that combine kernel smoothing techniques with axiomatic functional estimation.

 

EWEPAPlenary5

GAMS Tutorial

gams_s

 

This file contains the set of GAMS codes that was developed for a GAMS tutorial at EWEPA XIV held in June 2015 in Helsinki Finland. Please see the codes inside the zip file.

 

Tutorial (files)

MATLAB code for Constrained Weighted Bootstrap with y-space objective function

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This is the code for Constrained Weighted Bootstrap with y-space objective function written by Daisuke Yagi (d.yagi@tamu.edu).

Constrained Weighted Bootstrap is developed by Hall & Huang (2001) and Du et al. (2013).

ExperimentSettings.m file is the summary of the setting of Monte Carlo Simulation. You can run this file after you set every parameters for the experiments. If you want to use the real data, then you can use LLKwSCtest.m directly, which is the main code for the whole estimation process. Other files are called from LLKwSCtest.m during the process of bandwidth selection and estimation.

Function to compute the CONVEX Regression of the response y on the data matrix x with weight p
Function NEEDS the PACKAGE cvx (see http://cvxr.com/cvx/) to be installed in the computer

 

 

Du, P., Parmeter, C.F., Racine, J.S. (2013). Nonparametric kernel regression with multiple predictors and multiple shape constraints. Statistica Sinica 23(3): 1347–1371.

 

 

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MATLAB code for Kernel Weighted Convex Regression

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This is the code for Kernel Weighted Convex Regression written by Daisuke Yagi (d.yagi@tamu.edu).

We have two versions of KWCR: fixed bandwidth and variable bandwidth (KNN). ExperimentSettings.m file is the summary of the setting of Monte Carlo Simulation. You can run this file after you set every parameters for the experiments.

If you want to use the real data, then you can use LLKwSCtest.m directly, which is the main code for the whole estimation process. Other files are called from LLKwSCtest.m during the process of bandwidth selection and estimation.

You need to install Optimization Toolbox – quadprog function in your MATLAB.

 

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MATLAB code for Convex Nonparametric Least Square with Efficient Algorithm

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The first version of this code was created by Bodhisattva Sen at Columbia University. What is presented here is a slightly adapted version to aid in the estimation of CNLS.

Adaptations done by
Andrew L. Johnson ajohnson@tamu.edu
Jose Luis Preciado jpreciado@tamu.edu
Daisuke Yagi d.yagi@tamu.edu

Function to compute the CONVEX 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*p);
Note that the vector of ones is NOT required in the data matrix

We also integrated the efficient algorithm developed by Lee, C.-Y. (2014). This will help to reduce the computational time.

 

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