Seminars/presentation
December 17 - Osaka University - Modeling, Estimation and Interpretation: Using Census of Manufacturing Data to Gain Productivity Insights
December 19th, 2018
Census of manufacturing data is gathered by statistical agency around the world and can be used to benchmark performance within industries or summarize production behavior within industries. In this talk, I present results our laboratory have found for data from the U.S., Japan and Chile. I present some of the modeling challenges and limitations of the data. I focus on the use of nonparametric shape constrained production function estimators and[...]
December 14 - Operations Research Society of Japan - Kansai Chapter - Computational Complexity of Shape Constrained Estimation
December 19th, 2018
Production function estimation using nonparametric shape constrained methods can be computationally challenging. For the standard additive error model, y = f(x) + ε, with monotonicity and concavity constraints imposed at each observation, the associated programming problem has a quadratic objective function and linear inequality constraints and thus is easy from a computational complexity point of view. However, the number of constraints grows at the rate n2. Thus, estimation on a[...]
September 6 - Penn State - Shaped constrained estimation and some applications
December 19th, 2018
Assumptions motivated by either logical or existing theory can be imposed during model estimation to restrict the feasible region of the parameters. The restrictions, implemented as shape constraints, may not provide any benefits in an asymptotic analysis, but will improve the estimator’s finite sample performance. This paper briefly reviews an illustrative set of research on shape[...]
June 12-15 - North American Productivity Workshop - Axiomatic Nonparametric Production Functions: Relaxing Concavity and Homotheticity
May 30th, 2018
We develop a new approach to estimate a production function based on the economic axioms of the Regular Ultra Passum law and convex non-homothetic input isoquants. Central to the development of our estimator is stating the axioms as shape constraints and using shape constrained nonparametric regression methods.
We implement this approach using data from the Japanese corrugated cardboard industry from 1997-2007. Using this new approach, we find most productive scale size[...]
May 17 – Center for Operations Research and Econometrics: UC Louvain – Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries
May 30th, 2018
We examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as Shape Constrained Kernel-weighted Least Squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate.
In addition, we propose a test to validate whether shape constraints are correctly specified.
The competitiveness of SCKLS is shown in a comprehensive simulation study.[...]
May 14 – KU Leuven – Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries
May 30th, 2018
We examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as Shape Constrained Kernel-weighted Least Squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate.
In addition, we propose a test to validate whether shape constraints are correctly specified.
The competitiveness of SCKLS is shown in a comprehensive simulation study.[...]
May 4-7 - POMS - Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries
May 30th, 2018
We examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as Shape Constrained Kernel-weighted Least Squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate.
In addition, we propose a test to validate whether shape constraints are correctly specified.
The competitiveness of SCKLS is shown in a comprehensive simulation study.[...]
Jan 28-Feb 2, 2018 – Shape-Constrained Methods: Inference, Applications, and Practice – Nonparametric S-shape Estimation: Modeling firm expansion and consolidation in the Japanese Cardboard Industry
May 30th, 2018
We develop a new approach to estimate a production function based on the economic axioms of the Regular Ultra Passum law and convex non-homothetic input isoquants. Central to the development of our estimator is stating the axioms as shape constraints and using shape constrained nonparametric regression methods.
We implement this approach using data from the Japanese corrugated cardboard industry from 1997-2007. Using this new approach, we find most productive scale[...]
October 22-25 - Informs Annual Conference - DEA Track - Shape Constrained Estimation of Production Functions
January 20th, 2018
Nonparametric estimation methods avoid functional form misspecification. However, the flexibility of nonparametric methods often cause difficulties in interpreting production function estimates. However, microeconomic theory provides additional structure for modeling a production or cost function which can be interpreted as shape constraints. Several nonparametric shape constrained estimators have been proposed that combine the advantage of avoiding functional misspecification with improving the interpretability of estimation results. We will review the recent work[...]
June 19 - Lancaster University - Regulating Local Monopolies in Electricity Distribution: An Application of Shape Constrained Regression
April 13th, 2017
The Finnish electricity market has a competitive energy generation market and a monopolistic distribution system. To regulate the local monopoly power of network operators, the government regulator uses frontier estimation methods to identify excessive distribution costs, taking into account outputs and the operating environment. We describe the new regulatory system developed for the Finnish regulator, which is based on shape constrained nonparametric functional estimation and utilizes panel data to detect[...]
June 16 - Aston University - One day Workshop on Stochastic Nonparametric Envelopment of Data and Contextual Variables
April 13th, 2017
More details to come.[...]
March 6 – Department of Industrial and Systems Engineering, Virginia Tech – Regulating Local Monopolies in Electricity Distribution: An Application of Shape Constrained Regression
April 13th, 2017
The Finnish electricity market has a competitive energy generation market and a monopolistic distribution system. To regulate the local monopoly power of network operators, the government regulator uses frontier estimation methods to identify excessive distribution costs, taking into account outputs and the operating environment. We describe the new regulatory system developed for the Finnish regulator, which is based on shape constrained nonparametric functional estimation and utilizes panel data to detect[...]
February 1 – Department of Economics, Rice University – Regulating Local Monopolies in Electricity Distribution: An Application of Shape Constrained Regression
February 1st, 2017
The Finnish electricity market has a competitive energy generation market and a monopolistic distribution system. To regulate the local monopoly power of network operators, the government regulator uses frontier estimation methods to identify excessive distribution costs, taking into account outputs and the operating environment. We describe the new regulatory system developed for the Finnish regulator, which is based on shape constrained nonparametric functional estimation and utilizes panel data to detect[...]
November 14 - Informs Annual Meeting - Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data
November 25th, 2016
I have presented this paper at several conferences recently.
Abstract: In this paper we propose selecting an estimator based on a weighting of its in-sample and predictive performance on actual application datasets. For simulated data, we find that our proposed estimator has the lowest weighted errors. For actual data, specifically the 2010 Chilean Annual National Industrial Survey, a Cobb-Douglas specification describes at least 90% as much variance as the best[...]
August 12 - Institute of Manufacturing, National Cheng Kung University – Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data
August 13th, 2016
Abstract: In this paper we propose selecting an estimator based on a weighting of its in-sample and predictive performance on actual application datasets. For simulated data, we find that our proposed estimator has the lowest weighted errors. For actual data, specifically the 2010 Chilean Annual National Industrial Survey, a Cobb-Douglas specification describes at least 90% as much variance as the best alternative estimators in practically all cases considered.[...]
August 4 - Hitotsubashi Summer Institute 2016 - Measuring Output and Estimating Production Functions for Hospitals: Analysis of U.S. Data from 2004-2011
August 5th, 2016
Hitotsubashi Presentation[...]
July 21 and 28 - Osaka University - Lectures on Regression as a Special Case of Quadratic Programming
June 30th, 2016
Students in operations research and industrial engineering typically study linear and non-linear programming. Whereas regression is more commonly used in the fields of statistics and econometrics. This lecture will describe the relationship between the two methodologies.[...]
July 4 - Data Envelopment Analysis International Conference - Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data
June 30th, 2016
Abstract: In this paper we propose selecting an estimator based on a weighting of its in-sample and predictive performance on actual application datasets. For simulated data, we find that our proposed estimator has the lowest weighted errors. For actual data, specifically the 2010 Chilean Annual National Industrial Survey, a Cobb-Douglas specification describes at least 90% as much variance as the best alternative estimators in practically all cases considered.
Conference Website[...]
June 15 - North American Productivity Workshop - Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data
June 30th, 2016
Abstract: In this paper we propose selecting an estimator based on a weighting of its in-sample and predictive performance on actual application datasets. For simulated data, we find that our proposed estimator has the lowest weighted errors. For actual data, specifically the 2010 Chilean Annual National Industrial Survey, a Cobb-Douglas specification describes at least 90% as much variance as the best alternative estimators in practically all cases considered.
Presentation Slides
http://www.napw2016.com/index.html[...]
May 18 - Nonparametric statistical inference under shape constraints Workshop - Production Function Estimation Using Shape Constrained Estimators
June 30th, 2016
Production functions are economic models to characterize the relationship between resources consumed and output produced for production processes. Economic theories of production rarely provides guidance for a particular functional form; however, many economic theories provide shape restrictions on the production function. The specific shape restrictions vary across production processes. This talk will provide an overview of the most common shape restrictions and motivation for their use.[...]
April 8 - Georgia Tech - Large Scale Benchmarking of Manufacturing Performance
April 19th, 2016
Establishment level manufacturing data are gathered from several countries around the world. These datasets are gathered by the Census Bureau of the respective countries and is an exhaustive Census of all establishments in some years. For example the U.S. Census Bureau performs a full Census of all manufacturing establishments in years ending in 2 and 7 and performs a survey sampling only 15 percent of establishments in other years.
This[...]
December 2 - Workshop 2015 -Advances in DEA Theory and Applications - Predictive Efficiency Analysis: A Study of U.S. Hospitals
January 5th, 2016
Healthcare costs are higher in the U.S. then anywhere else in the world. A significant portion of the costs are generated in hospitals. We investigate both the efficiency and the effectiveness of U.S. community hospitals using the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project 2009-2011 Nationwide Inpatient Sample, a data set which contains all discharges from an approximate 20% sample of hospitals.
Here efficiency is the[...]
November 3 - Informs Annual Meeting - Shape Constrained Kernel Weighted Least Squares for the Estimation of Production Functions
January 5th, 2016
This paper proposes a unifying model and estimator we call Shape Constrained Kernel-weighted Least Squares (SCKLS). We show the relationship between the SCKLS estimator and both the Convex Nonparametric Least Squares (CNLS) and Du’s estimators. Specifically, the SCKLS estimator converges to the CNLS estimator as the bandwidth goes to zero. We compare the performance of the three estimators (SCKLS, CNLS, and Du’s estimator) via Monte Carlo simulations.[...]
September 24 - Penn State University: Production Economics for Performance Evaluation of Engineered Systems: The Example of Finnish Electricity Distribution
October 7th, 2015
The Finnish electricity market has a competitive energy generation market and a monopolistic transmission system. To regulate the local monopoly power of network operators, the government regulator uses frontier estimation methods. We describe the new regulatory system developed for the Finnish regulator, which is based on the method Stochastic semi-Nonparametric Envelopment of Data (StoNED) and utilizes panel data to detect the excessive costs from random noise.[...]
July 14th - Material Handling Teacher's Institute - University of Wisconsin - Madison
July 21st, 2015
This presentation describes the graduate and undergraduate material handling course taught at Texas A&M. Particular emphasis is given to the content description and the case study project that pairs 2 undergraduate and 2 graduate students in teams to participate in MHI's case study competition.
Presentation[...]
July 9th - Osaka University: Research Topics in Industrial Engineering and Operations Research
July 13th, 2015
This presentation describes some common research topics in industrial engineering and operations research. It focuses on the Department of Industrial and Systems Engineering at Texas A&M University and my laboratory. Its purposes is to further the understanding between Osaka University and Texas A&M.
Presentation[...]
EWEPA: Shape Constrained Nonparametric Estimation of Production Functions
June 21st, 2015
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[...]
November 9th - Informs Annual Conference: A Multivariate Seminonparametric Bayesian Concave Regression Method to Estimate Stochastic Frontiers
November 15th, 2014
This presentation discusses a method that incorporates the latest advances in the Bayesian constrained regression literature offering an alternative to the current Least Squares-based and Kernel Regression-based Stochastic frontier constrained estimation methods, both in terms of runtime and of data capacity.
Although monotonicity constraints can be applied in a simple manner by the specification of sign constraints on the regression coefficients in both parametric and nonparametric settings, estimation of concavity-constrained[...]
October 4 and 5: College Industry Council on Material Handling Education Semi-annual Meeting in San Diego
October 16th, 2014
The College Industry Council on Material Handling Education (CICMHE) is a group of 15 academic and 8 industry members that work to increase awareness, understanding, exploration, and development of material handling and logistics through projects and events.
CICMHE meet in San Diego as part of the MHI's annual conference. The two-day event resulted in a set of projects MHI will fund to promote material handling and logistics education along with allowing[...]
September 25: The 6th Helsinki Workshop on Efficiency and Productivity Analysis, Helsinki Finland
September 29th, 2014
Bayesian StoNED or Multi-variate Bayesian Convex Regression with inefficiency: Two sides of the same coin.
Bayesian methods allow researchers to simulate regression models to investigate the effects of prior distributional assumptions and modeling restrictions on posterior estimates of model parameters. Here we try to develop a Bayesian version of Stochastic Nonparametric Envelopment of Data (StoNED). There are strong similarities between StoNED and Multi-variate Bayesian Convex Regression (MBCR) proposed by Hannah and[...]
June 23-27 International Material Handling Research Colloquium - Cincinnati, OH
September 29th, 2014
The International Material Handling Research Colloquium (IMHRC) purpose is to share world-class research accomplishments, projects and trends in the field of material handling, facility logistics and intralogistics.
It aims to facilitate dialog and collaborative research by teams of university researchers on leading edge topics of interest to end users as well as technology and solutions providers. The Colloquium operates on an immersion philosophy of complete participation by all participants in[...]
June 17: University of Science and Technology of China - Hefei
September 29th, 2014
Stochastic nonparametric approach to efficiency analysis: A Unified Framework
Efficiency analysis is an essential and extensive research area that provides answers to such important questions as: Who are the best performing firms and can we learn something from their behavior? What are the sources of efficiency differences across firms? Can efficiency be improved by government policy or better managerial practices? Are there benefits to increasing the scale of operations? These are[...]
June 4th: North American Productivity Workshop
September 29th, 2014
The North American Productivity Workshop (NAPW) is a biennial conference held in North America in even years and its sister conference European Workshop on Efficiency and Productivity Analysis (EWEPA) is held in odd years. It brings together researchers in economics, operations research, management science, engineering and a wide variety of application areas to discuss the latest innovations in efficiency and productivity research.
The 9th NAPW was held in Ottawa Canada[...]
May 15: Harvard Business School
September 29th, 2014
10 Years of the World Management Survey: Lessons and Next Steps
How Much Does Management Affect Productive Performance? New Insights from a semi-nonparametric Analysis of the World Management Survey
Abstract
It remains a challenge to demonstrate the effects of management for improving performance beyond simply relying on case studies and anecdotal evidence. The World Management Survey presents a unique opportunity to look more closely at the relationship between management and performance. This[...]
March 7: Aalto University
September 29th, 2014
Department of Service and Information Economy - Helsinki, Finland
Analysis and Control of Batch Order Picking Processes Considering Picker Blocking
Abstract:
Order picking operations play a critical role in the order fulfillment process of distribution centers (DCs). Picking a batch of orders is often favored when customers’ demands create a large number of small orders, since the traditional single order picking process results in low utilization of order pickers and significant[...]
March 5: University of Leuven
September 29th, 2014
Group for the Advancement of Revealed Preferences - Kortrijk, Belgium
Orthogonality conditions for identification of joint production technologies
Axiomatic nonparametric approach to the estimation of stochastic distance functions
Abstract:
The classic econometric approach treats productivity as a residual term of the standard microeconomic production model. Critics of this approach argue that productivity shocks correlate with the input factors that are used as explanatory variables of the regression model, which causes an[...]
February 19: Queen's University
August 4th, 2014
Finance Research Group Seminar - Belfast, Northern Ireland
Benchmarking managerial performance: a Stochastic semi-Nonparametric Envelopment of Data Approach
The Finnish electricity market has a competitive energy generation market and a monopolistic transmission system. To regulate the local monopoly power of network operators, the government regulator uses frontier estimation methods (e.g., Stochastic Frontier Analysis (SFA) and nonparametric Data Envelopment Analysis (DEA)) to identify excessive transmission costs, taking into account outputs and the[...]
November 21: University of Southern Denmark
November 29th, 2013
Department of Business and Economics
Regulating Local Monopolies in Electricity Transmission: A Real-world Application of the StoNED Method
Abstract:
The Finnish electricity market has a competitive energy generation market and a monopolistic transmission system. To regulate the local monopoly power of network operators, the government regulator uses frontier estimation methods (e.g., Stochastic Frontier Analysis (SFA) and nonparametric Data Envelopment Analysis (DEA)) to identify excessive transmission costs, taking into account outputs and[...]