Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025African Journal of Mathematics and Statistics StudiesOpen Access

Robust Estimation in Simultaneous Equation Models: Addressing Multicollinearity and Heteroscedasticity through Adaptive Penalized GMM Techniques

View Full Paper
Ask AI
Bookmark
Share

Authors

NON. C. OkekeSOSamuel Olayemi OlanrewajuUniversity of AbujaZMZainal Abidin MohammedUniversiti Putra Malaysia

Discussion

Loading...

Member takes

Implication

This research evaluates robust methods for simultaneous equation models, addressing multicollinearity and heteroscedasticity to enhance estimation accuracy.

Key Points

  • The proposed estimators significantly outperform traditional methods under severe assumption violations.
  • HCGMM is identified as the most robust estimator with the lowest RMSE and bias in most scenarios.
  • Adaptive Ridge IV and Generalized Two-Stage Adaptive Elastic-Net show strong adaptability to complex data structures.
  • This work highlights the importance of reforming estimation techniques in econometric modeling for applied research.

Cite This Study

Okeke et al. (2025) studied this question.

synapsesocial.com/papers/68c1b1a154b1d3bfb60e94f0https://doi.org/10.52589/ajmss-u1lhedsz
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Robust Approach to Heteroscedasticity, Error Serial Correlation and Slope Heterogeneity in Linear Models with Interactive Effects for Large Panel Data2022 · 9 citations
  2. 2A Comparison of Different Ridge Parameters under Both Multicollinearity and Heteroscedasticity2019 · 10 citations
  3. 3HETEROSCEDASTICITY CORRECTION MEASURES IN STOCHASTIC FRONTIER ANALYSIS2024 · 5 citations
  4. 4The modified Liu-ridge-type estimator: a new class of biased estimators to address multicollinearity2020 · 27 citations
  5. 5On the almost unbiased generalized liu estimator and unbiased estimation of the bias and mse1995 · 152 citations