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Journals / EKOIST Journal of Econometrics and Statistics / 2026 / Issue: 24

HYBRID CODED GENETIC ALGORITHM TOWARDS DETERMINING SMOOTH TRANSITION AUTOREGRESSIVE MODELS

DÜZGÜN GEÇİŞ OTOREGRESİF MODELLERİN BELİRLENMESİNE YÖNELİK MELEZ KODLAMALI GENETİK ALGORİTMA

Serkan Taştan(CUMHURIYET UNIV)
Nilgün Çil(İstanbul Üniveristesi)
Pages
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Publication Date
2016-04-29
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Better-fitted models than the benchmark model are obtained with the proposed approach.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, the specification of the smooth transition autoregressive (STAR) models as an optimization problem has been handled with genetic algorithms.
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Key Findings & Data:In this context a hybrid-coded genetic algorithm is used.
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Conclusion & Impact:Better-fitted models than the benchmark model are obtained with the proposed approach.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Smooth Transition Autoregressive · STAR · Genetic Algorithms

Abstract

In this paper, the specification of the smooth transition autoregressive (STAR) models as an optimization problem has been handled with genetic algorithms. In this context a hybrid-coded genetic algorithm is used. The success of the genetic algorithm based approach is evaluated via a benchmark STAR model determined by conventional method. Better-fitted models than the benchmark model are obtained with the proposed approach.

Özet

Bu makalede, düzgün geçiş otoregresif (STAR) modellerin belirlenmesi süreci bir optimizasyon problemi olarak genetik algoritmalar ile ele alınmıştır. Bu bağlamda melez kodlamalı bir genetik algoritma kullanılmıştır. Genetik algoritma tabanlı yaklaşımın başarısı, geleneksel yöntemle belirlenen bir referans STAR modeli üzerinden değerlendirilmiştir. Sunulan yaklaşım ile referans modelden daha iyi uyuma sahip modeller elde edilmiştir.

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Cite This Article

Taştan, S., Çil, N. (2016). HYBRID CODED GENETIC ALGORITHM TOWARDS DETERMINING SMOOTH TRANSITION AUTOREGRESSIVE MODELS. EKOIST Journal of Econometrics and Statistics, Issue: 24. https://ajindex.com/articles/1636342

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References (2)

  1. Balcombe, K. G. (2005). Model Selection Using Information Criteria and Genetic Algorithms. Computational Economics, 25(3), 207-228.
  2. Baragona, R., Battaglia, F., & Cucina, D. (2004). Fitting piecewise linear threshold autoregressive models by means