Ongoing work

Ongoing work

Iterative S-shape production function estimation
Iterative S-shape production function estimation
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[...]
Sweet or Sour? The Potential for U.S.-Cuban Trade in Sugar
Sweet or Sour? The Potential for U.S.-Cuban Trade in Sugar
This project seeks to analyze the potential for U.S.-Cuban sugar trade with a particular focus on Cuban sugar production. Cuba has historically been an important global sugar producer and U.S. sugar producers are sensitive to foreign competition. Furthermore, the potential for Cuban liberalization and subsequent possible inflow of foreign investment makes analysis of this industry important. The project’s implications are significant for U.S. sugar producers, U.S. national agricultural policy, and[...]
Evaluating Production Function Estimators on Manufacturing Survey Data
Evaluating Production Function Estimators on Manufacturing Survey Data
Organizations like census bureaus rely on non-exhaustive surveys to estimate industry population-level production functions. In this paper we propose selecting an estimator based on a weighting of its in-sample and predictive performance on actual application datasets. We compare Cobb-Douglas functional assumptions to existing nonparametric shape constrained estimators and a newly proposed estimated presented in this paper. For actual data, specifically the 2010 Chilean Annual National Industrial Survey, a Cobb-Douglas specification[...]
How inefficient are U.S. hospitals? What changes can lead to improvement?
How inefficient are U.S. hospitals? What changes can lead to improvement?
We use U.S. hospital data from 2004 to 2011 to estimate a cost function using a Bayesisan semi-nonparametric method that allows for a heteroskedastic inefficiency. Moreover, we evaluate what is the impact of variables, such as region and hospital size, on cost estimating both the size and robustness of the variables in terms of reducing cost for hospitals.[...]
Adaptively Partitioned Convex Nonparametric Least Squares
Adaptively Partitioned Convex Nonparametric Least Squares
This research overcomes both the decreased accuracy of Convex Adaptive Partitioning on real production survey datasets and the cross-validation performance challenges of CNLS to create a robust and scalable adaptive partitioning-based convex regression method. We discover that real production datasets often contain local monotonicity violations, which affect CAP’s ability to propose feasible basis region splits. Moreover, we note that CNLS’s error minimization strategy within the observed dataset results in poor estimations[...]
Shape Constrained Kernel-weighted Least Squares (SCKLS)
Shape Constrained Kernel-weighted Least Squares (SCKLS)
SCKLS (shape constrained kernel-weighted least squares) estimator integrates kernel-weighting to convex nonparametric least squares. Kernel regression is one of the powerful nonparametric estimation methods. By imposing more weight to some closer points, kernel regression helps to avoid over-fitting although it requires the tuning parameter, bandwidth. By imposing some shape constraints such as monotonicity and concavity, we propose SCKLS estimator and apply it to estimate production function with simulated and real[...]
Shape constrained Semi-nonparametric Stochastic Frontiers estimation using a local maximum likelihood approach
Shape constrained Semi-nonparametric Stochastic Frontiers estimation using a local maximum likelihood approach
We propose a shape-constrained production function estimator starting from the method described in Kumbhakar, Park, Simar, Tsionas 2006 (KPST). We maximize the loglikelihood of a local linear estimator at each observation. We estimate the parameters for the noise and inefficiency distributions that are potentially heteroscedasticity. The challenge to imposing shape constraints, such as monotonicity and concavity, is we need to jointly esitmate the maximum likelihood function for all observations while[...]
Multi-variate Bayesian Convex Regression with Inefficiency
Multi-variate Bayesian Convex Regression with Inefficiency
This research builds on Nonparametric Multi-variate Bayesian Convex Regression to develop a method to estimate shape constrained production frontiers with heteroskedastic inefficiency distributions that scales up to thousands of observations. We propose a Bayesian method which allows the estimation of a semiparametric production frontiers with a flexible inefficiency distribution, to use panel data and to measure the impact of environmental variables. A Metropolis-Hastings framework is considered to compute smoothed and non-smooth[...]
Shape Restricted Estimation of the Power Curve for a Wind Turbine
Shape Restricted Estimation of the Power Curve for a Wind Turbine
The estimation of the power curve provides an application for methods to estimate production functions consistent with the regular ultra passum law. It is well known based on fluid dynamics and kinetic energy that the power generated by a wind turbine follows power curve that appears to be S-shaped. In production economics we also believe the production function has a similar S-shaped when the regular ultra-passim law proposed by Ranger[...]
Orthogonality Conditions for Identification of Joint Production Technologies: Axiomatic Nonparametric Approach to the Estimation of Stochastic Distance Functions
Orthogonality Conditions for Identification of Joint Production Technologies: Axiomatic Nonparametric Approach to the Estimation of Stochastic Distance Functions
The selection of the direction in the directional distance function provides a way to address some endogeneity issues in the estimation of production functions. In the summer of 2013, Timo Kuosmanen, Christopher Parmeter, and I taught a week long Ph.D. course before the EWEPA conference at Aalto University. Out of this activity we have had a variety of discussion on various research topics. One of which is the issue of endogeneity[...]
Stochastic semi-Nonparametric Envelopment of Data
Stochastic semi-Nonparametric Envelopment of Data
StoNED is the unifying framework for efficiency analysis in which Data Envelopment Analysis and Stochastic Frontier Analysis are specific special cases. The literature of productive efficiency analysis is divided into two main branches: the parametric Stochastic Frontier Analysis (SFA) and nonparametric Data Envelopment Analysis (DEA). Stochastic Nonparametric Envelopment of Data (StoNED) is a new frontier estimation framework that combines the virtues of both DEA and SFA in a unified approach[...]
Best Practices in Warehousing
Best Practices in Warehousing
Over a 10 year period, Texas A&M and Georgia Tech collaborated to gather detailed performance data on hundreds of warehouses. Using efficiency analysis methods, drivers of warehouse performance are identified.   Johnson, A.L. and L.F. McGinnis, 2011. “Performance Measurement in the Warehousing Industry” IIE Transactions.43(3): 203-215. Johnson, A. L., W.-C. Chen and L. F. McGinnis, 2010. “Larege-scale Internet Benchmarking: Technolgoy and Application in Warehousing Operations” Computers in Industry 61:280-286.[...]
Production Synergies in Hospitals
Production Synergies in Hospitals
We analyze the 2008 National Inpatient Sample of the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project. We find that small hospitals experience productivity losses from the joint production of minor and major diagnostic procedures. paper[...]
Nuclear Medicine Production and Delivery
Nuclear Medicine Production and Delivery
Radioisotope F-18, used for diagnosing and monitoring many types of cancers, requires careful coordination of production and delivery. The method we develop shows improvement in terms of both time and cost. paper[...]
National Oilwell Varco - Manufacturing System
National Oilwell Varco - Manufacturing System
National Oilwell Varco is a worldwide leader in the design, manufacture and sale of equipment and components used in oil & gas drilling and production. University of Wisconsin-Madison, Texas A&M Unviersity, and Penn State are working together to develop an Manufacturing System that will allow NOV to maintain their position as worldwide leader. The project has two primary components: 1) develop tools to support decision making regarding manufacturing organizational[...]