Journal of Economic Surveys· 2025Q1· Review
Distributional and Tail‐Dependent Perspectives in Economic Relationships: A Review of Quantile Regression Application
- 28citations
- Q1SCImago
- 2025year
Short summary
Quantile regression (QR) offers a robust alternative to mean regressions for estimating non-normal economic variables, providing superior data insights by analyzing the entire distribution, not just the average.
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Key points
- Quantile regression (QR) is a robust alternative to mean regressions for economic variables with distributional heterogeneity.
- The review synthesizes QR's evolution, methodology, and empirical applications in economics over the past decade.
- Bibliometric analysis of 53 Scopus studies highlights QR's ability to provide superior data insights compared to OLS.
- QR advances econometric debates by offering distribution-sensitive modeling strategies.
AI-generated from the title and abstract; the full text is not read.
Abstract
ABSTRACT There is an increased proportion of studies using quantile‐based regression methodology (QR) in economics. They offer a robust alternative to classical mean regressions, which can estimate non‐normal variables with distributional heterogeneity in the dependent variable. This study synthesizes the theoretical foundations, methodological advancements, and empirical application of QR in economics that traces the evolution from the foundational work of Koenker and Basset. The targeted studies are from Scopus. Bibliometrix library in R is used for bibliometric analysis, and Structured Literature Revie (SLR) is conducted on selected studies. The Scopus query started with 250 studies and was finalized at 53 studies that focused on the motivation of using QR, comparison of ordinary least squares (OLS), and QR in economics. The systematic review has spanned the past decade. The consolidation of fragmented evidence showed that QR can advance econometric debates by providing superior data insights. The insights presented in this review are aimed at bridging the gap between econometric development and applied economic policy research. This paper contributes to a deeper understanding of distribution‐sensitive modeling strategies, offering valuable implications for economists in academia, government, and industry.
The authors' abstract, as published at the source. Journal of Economic Surveys, 2025 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics