Semiparametric and Nonparametric Approaches in Applied Economics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 36

Special Issue Editors


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Guest Editor
Lingnan College, Sun Yat-Sen University, Guangzhou 510275, China
Interests: semiparametric and nonparametric estimation; microeconometric applications; consumption behavior; household finance; tobit models

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Guest Editor
College of Economics, Shenzhen University, Shenzhen 518060, China
Interests: non parametric statistics applied in the financial field

Special Issue Information

Dear Colleagues,

This Special Issue explores the growing significance of semiparametric and nonparametric methodologies in addressing complex economic problems. Unlike traditional parametric models, these approaches relax rigid assumptions about functional forms or error distributions, offering greater flexibility to capture nonlinear relationships, heterogeneous effects, and high-dimensional data structures. Contributions are encouraged in areas such as policy evaluation, causal inference, forecasting, risk analysis, and machine learning integration. By leveraging data-driven techniques, these methods enhance robustness in scenarios with limited dependent variables, model uncertainty, missing data, or complex dependencies. The issue seeks to highlight cutting-edge research that advances methodological frameworks and demonstrates practical applications in labor, health, finance, environmental economics, and development economics. Submissions may address challenges like scalability, interpretability, and the balance between flexibility and computational efficiency. Theoretical innovations, simulation studies, and empirical case studies are welcome, particularly those addressing real-world economic questions through innovative estimation, inference, or model selection strategies. The Special Issue aims to bridge the gap between econometric theory and applied research, fostering dialogue on how flexible modeling can improve evidence-based decision-making in economics.

Prof. Dr. Xianbo Zhou
Prof. Dr. Jinbo Huang
Guest Editors

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Keywords

  • semiparametric methods
  • nonparametric econometrics
  • limited dependent variable models
  • causal inference
  • machine learning in economics
  • policy evaluation
  • robust estimation
  • heterogeneous treatment effects
  • high-dimensional data
  • model flexibility

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Published Papers

This special issue is now open for submission.
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