Efficient Test for Threshold Regression Models in Short Panel Data

Authors

  • DOUNIA BOURZIK LMA, FSTT, Abdelmalek Essaadi University, Tetouan, Morocco
  • AZIZ LMAKRI AIMCE laboratory, ENSAM, Hassan II University, Casablanca, Morocco
  • AMAL MELLOUK Regional Center for Education and Training Trades, Tangier, Morocco
  • ABDELHADI AKHARIF LMA, FSTT, Abdelmalek Essaadi University, Tetouan, Morocco

DOI:

https://doi.org/10.19139/soic-2310-5070-3091

Keywords:

Threshold Regression Model, Local Asymptotic Normality, Local Asymptotic Linearity, Panel Data, Gaussian Tests, Adaptive Tests

Abstract

In this paper, we propose locally and asymptotically optimal tests (as defined in the Le Cam sense) that are parametric, Gaussian, and adaptive. These tests aim to address the problem of testing the classical regression model against the threshold regression model in short panel data, where n is large and T is small. The foundation of these tests is the Local Asymptotic Normality (LAN) property. We derive the asymptotic relative efficiencies of these tests, specifically in comparison to the Gaussian parametric tests. The results demonstrate that the adaptive tests exhibit higher asymptotic power than the Gaussian tests. Additionally, we conduct simulation studies and analyze real data to evaluate the performance of the suggested tests, and the results confirm their excellent performance.

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Published

2025-12-15

How to Cite

BOURZIK, D., LMAKRI, A., MELLOUK, A., & AKHARIF, A. (2025). Efficient Test for Threshold Regression Models in Short Panel Data. Statistics, Optimization & Information Computing, 15(3), 1611–1631. https://doi.org/10.19139/soic-2310-5070-3091

Issue

Vol. 15 No. 3 (2026)

Section

Research Articles

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