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 examples of important questions we hope to resolve with efficiency analyses.
Efficiency analysis is an interdisciplinary field that spans such disciplines as economics, econometrics, operations research and management science, and engineering, among others. The methods of efficiency analysis are utilized in several fields of application including agriculture, banking, education, environment, health care, energy, manufacturing, transportation, and utilities, among many others. Efficiency analysis is performed at various different scales. Micro level applications range from individual persons, teams, production plants and facilities to company level and industry level efficiency assessments. Macro level applications range from comparative efficiency assessments of production systems or industries across countries to efficiency assessment of national economies. Indeed, efficiency improvement is one of the key components of productivity growth (e.g., Färe et al., 1994), which in turn is the primary driver of economic welfare.
Unfortunately, there currently is no commonly accepted methodology of efficiency analysis, but the field is divided between two competing approaches: Data envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). Bridging the gap between axiomatic DEA and stochastic SFA was for a long time one of the most vexing problems in the field of efficiency analysis. The recent works on convex nonparametric least squares (CNLS) by Kuosmanen (2008), Kuosmanen and Johnson (2010), and Kuosmanen and Kortelainen (2012) have led to the full integration of DEA and SFA into a unified framework of productivity analysis, which we refer to as stochastic nonparametric envelopment of data (StoNED).
We see the development of StoNED as a paradigm shift for efficiency analysis. It is no longer necessary to decide if modeling noise is more important than imposing axioms of production theory: we can do both using StoNED. The unified framework of StoNED offers deeper insights to the foundations of DEA and SFA, but it also provides a more general and flexible platform for efficiency analysis and related themes such as frontier estimation and production analysis. Further, a number of extensions to the original DEA and SFA methods have been developed over the past decades. The unified StoNED framework allows us to combine the existing tools of efficiency analysis in novel ways across the DEA-SFA spectrum, facilitating new opportunities for further methodological development.
This presentation’s main objective is to describe CNLS and StoNED methods.