SPATIAL ANALYSIS OF THE SOVEREIGN CREDIT RATINGS: A CASE OF GIPSI COUNTRIES

Abstract

Sovereign credit ratings are monitored globally because they reflect the forward-looking estimate of the default probability. In addition, they are widely accepted as an indicator of sovereign risk. Many studies try to find the determinants of the credit ratings from economic, financial or political perspectives. While many different econometric methods (like ordinary least square, ordered response model or ordered probit models) are used in these studies, ignoring the interdependency between the countries can cause a major problem. This study aims to contribute to the related literature by applying spatial methods for credit ratings. In contrast to the conventional models, spatial models consider the spillover effects between the countries. For this reason, quarterly data for GIPSI (Greece, Ireland, Portugal, Spain and Italy) countries are used from 2003 to 2021. LM (Lagrange Multiplier) test and the LR (Likelihood Ratio) tests support that the spatial autoregressive model (SAR) is appropriate. According to estimations, the explanatory variables (GDP per capita, international reserves, GDP growth, primary balance, current account balance and government debt) are found to be statistically significant. In addition, the spatial autoregressive coefficient (ρ) is significant, which provides the existence of spatial interaction.

Keywords

• Spatial Econometrics
• Sovereign Credit Ratings
• GIPSI Countries

ÜLKE KREDİ DERECELENDİRMENİN MEKÂNSAL ANALİZİ: GIPSI ÜLKELERİ

Abstract

Ülke kredi notları, temerrüt olasılığının ileriye dönük tahminini yansıttıkları için küresel olarak izlenmektedir. Ayrıca, ülke riskinin bir belirleyicisi olarak da kabul edilmektedir. Birçok çalışma, kredi notlarının belirleyicilerini ekonomik, finansal veya politik açıdan bulmaya çalışmaktadır. Bu çalışmalarda birçok farklı ekonometrik yöntem (en küçük kareler, sıralı tepki modeli veya sıralı probit modelleri gibi) kullanılırken, ülkeler arasındaki karşılıklı bağımlılığın göz ardı edilmesi büyük bir soruna neden olabilmektedir. Bu çalışma, kredi notları için mekânsal yöntemler uygulayarak ilgili literatüre katkı sağlamayı amaçlamaktadır. Geleneksel modellerin aksine, mekânsal modeller, ülkeler arasındaki yayılma etkilerini dikkate almaktadır. Bu nedenle bu çalışmada GIPSI (Yunanistan, İrlanda, Portekiz, İspanya ve İtalya) ülkeleri için 2003-2021 dönemi için üçer aylık veriler kullanılmıştır. LM(Lagrance Multiplier) ve LR(Likelihood Ratio) testleri mekânsal otoregresif modelin uygun olduğunu desteklemektedir. Yapılan tahminlemelere göre, açıklayıcı değişkenler (kişi başına GSYİH, uluslararası rezervler, GSYİH büyümesi, faiz dışı denge, cari işlemler dengesi ve devlet borcu) istatistiksel olarak anlamlı bulunmuştur. Ayrıca mekansal etkileşimin varlığını kanıtlayan mekansal otoregresif katsayısı (ρ) anlamlı bulunmuştur.

Keywords

• Mekânsal Ekonometri
• Ülke Kredi Derecelendirme
• GIPSI Ülkeleri

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