Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2019 / Cilt: 35 - Sayı: 3

Multivariate Adaptive Regression Splines (Mars) Method For Unemployment in OECD Countries

Çok Değişkenli Uyarlanabilir Regresyon Eğrileri Yöntemi ile OECD Ülkelerindeki İşsizlik

Pages
46–51
Publication Date
2022-11-04
DOI
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AI Summary & Key Takeaways

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

Besides these results, the industrial production, the industrial value addedand current account balance are influential for a few years.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Unemployment is one of the most important macroeconomic problemsin all countries and it is very important task for identification of the keydeterminants of it.
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Key Findings & Data:In this context, data for the years2000-2017 were analyzed by using MARS method.
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Conclusion & Impact:Besides these results, the industrial production, the industrial value addedand current account balance are influential for a few years.
🔬Design:Modeling & Simulation
📍Scope:2017 Verileri
⚙️Variables:Domain Parameters

Abstract

Unemployment is one of the most important macroeconomic problemsin all countries and it is very important task for identification of the keydeterminants of it. Therefore, in recent years determining the factors affecting theunemployment is attracting the researcher. In this study, the factors affectingunemployment in Organization for Economic Co-operation and Development(OECD) countries were tried to be determined. In this context, data for the years2000-2017 were analyzed by using MARS method. For each year, we estimated theMultivariate Adaptive Regression Splines (MARS) models and we tracked theeffective predictors. According to our findings, the indicators Gross domesticproduct (Gdp), tax revenue rate, long term interest rate, saving rate and inflationusually have a significant impact on the unemployment rates. The annual growthrate of import, export and exchange rate do not influence the unemploymentratios. Besides these results, the industrial production, the industrial value addedand current account balance are influential for a few years.

Özet

İşsizlik tüm ülkelerde en önemli makroekonomik problemlerden biridir ve işsizliğin temel göstergelerinin belirlenmesi oldukça önemlidir. Bu nedenle son yıllarda işsizliği etkileyen faktörlerin belirlenmesi araştırmacıların ilgisini çekmiştir. Bu çalışmada OECD ülkelerindeki işsizlik oranlarını etkileyen faktörler belirlenmeye çalışıldı. Bu bağlamda 2000-2017 yıllarına ait veriler MARS metodu kullanılarak analiz edildi. Her bir yıl için MARS modelleri tahmin edildi ve etkili olan açıklayıcı değişkenler değerlendirildi. Sonuçlara göre Gayrı safi yurt içi hasıla (Gsyih), vergi gelir oranı, uzun vadeli faiz oranı, tasarruf oranı ve enflasyon göstergeleri genellikle işsizlik oranları üzerinde önemli bir etkiye sahiptir. Yıllık ithalat büyüme oranı, ihracat ve döviz kuru oranları işsizlik oranlarını etkilememektedir. Bu sonuçların yanı sıra, sanayi üretimi, sanayi katma değeri ve cari işlemler dengesi birkaç yıl için etkili bulunmuştur.

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Cite This Article

KOÇ, T., KOÇ, H., DÜNDER, E. (2022). Multivariate Adaptive Regression Splines (Mars) Method For Unemployment in OECD Countries. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 35 - Sayı: 3, 46–51. https://ajindex.com/articles/650137

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