Quantile methods for Stochastic Frontier Analysis

Posted: November 10, 2022 in Research, Research papers, Uncategorized
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Finally. This journey started in June 2020 in the North American Productivity Workshop, Miami Florida USA, (virtual due to covid-19). There were some papers on applying quantile methods to stochastic frontier analysis (SFA), and I publicly commented that something just didn’t feel in place. Not technical stuff, but conceptual issues. I started thinking and writing about it, and one year later in the next NAPW workshop (again virtual), June 2021, I presented “Quantile regression in Stochastic Frontier Analysis: some fundamental considerations” in the virtual North American Productivity Workshop (Miami Florida USA), where I was rather pessimistic about the applicability of quantile methods in SFA. The organizer of the workshop Chris Parmeter was also writing a piece on the topic, having his own concerns about whether Quantile Regression and SFA can co-exist. We initially discussed putting together a “symposium”, a small collection of papers around the subject in a journal, but we ended instead writing a monograph together. Which just got published. It is not just a review, but has new (valid) tools, an empirical application, and many open issues for further research.

The abstract goes

Quantile regression has become one of the standard tools of econometrics. We examine its compatibility with the special goals of stochastic frontier analysis. We document several conflicts between quantile regression and stochastic frontier analysis. From there we review what has been done up to now, we propose ways to overcome the conflicts that exist, and we develop new tools to do applied efficiency analysis using quantile methods in the context of stochastic frontier models. The work includes an empirical illustration to reify the issues and methods discussed, and catalogs the many open issues and topics for future research.

Here are the ToC

1 Introduction
I Where We Are
2 The Relation Between Conditional Quantiles and the Regression Function
3 Basics of Quantile Regression: The Independence Case
4 Where Quantile Regression and Stochastic Frontier Analysis Clash
5 Reconciling Quantile Regression with Stochastic Frontier Models
6 Likelihood-Based Quantile Estimation

II What We Can Do
7 The Corrected Q-Estimator
8 Quantile-Dependent Efficiency
9 From the Composite Error Term to Inefficiency: A Fundamental Result
10 Quantile Estimation and Inference with Dependence
11 An Empirical Application

III For the Road
12 Challenges Ahead
13 Summary and Concluding Remarks

The full text is at http://dx.doi.org/10.1561/0800000042 (pay-walled).

Click here to download front material, chapter 1, 12, 13 and references.


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