Türkiye'de Ekonomik Şoklar ve Krizler Bağlamında Enflasyon Öngörüsü: XGBOOST ve ARMA Yöntemlerinin Karşılaştırması

Abstract

Enflasyon, ekonomik istikrar ve büyüme üzerinde derin etkiler yaratan, temel bir makroekonomik göstergedir. Fiyatlar genel düzeyindeki süreklilik arz eden artışlar, yalnızca bireylerin satın alma güçlerini zayıflatmakla kalmayıp, ulusal ekonominin çeşitli sektörleri üzerinde de ciddi tehditler oluşturmaktadır. Dolayısıyla, enflasyonun doğru tahmini hem merkez bankaları hem de hükümetler için stratejik bir önem taşımaktadır. Bu çalışma, Türkiye’deki ekonomik şoklar ve kriz dönemlerinde, enflasyon tahmininde XGBoost ve ARMA modellerinin performansını incelemektedir. 1994 ekonomik krizi, 2001 finansal krizi, 2008 küresel finansal krizi ve 2018 döviz krizi gibi sık yaşanan krizler ve Türkiye'nin özgün makroekonomik koşulları göz önüne alındığında, enflasyonun doğru tahminini zorlaştırmaktadır. Çalışmada, kriz dönemleri de dahil olmak üzere farklı zaman dilimlerinde XGBoost makine öğrenimi algoritması ile ARMA modelinin performansı karşılaştırılmaktadır. Ampirik bulgular, XGBoost’un büyük veri setleri ve kriz dönemlerinde güçlü performans gösterdiğini, ancak geleneksel ARMA modelinin daha küçük veri setlerinde daha iyi sonuçlar verdiğini ortaya koymaktadır. Özellikle, ARMA modelinden gelen gecikmeli değişkenlerin XGBoost’a entegre edilmesiyle elde edilen tahmin modeli, kriz dönemlerinde ve tüm örneklem dönemi olan 1990:02-2024:06 arasında en etkili yöntem olarak belirlenmiştir. Bu sonuçlar, enflasyon tahmini için kullanılan modellerin veri yapısına duyarlılığını vurgulamakta ve farklı dönemlerdeki etkinliklerini ortaya koymaktadır.

Keywords

• XGBoost
• Enflasyon Öngörüsü
• ARMA Modeli

Inflation Forecasting in the Context of Economic Shocks and Crises in Türkiye: Comparison of XGBoost and ARMA Methods

Abstract

Inflation is a key macroeconomic indicator with profound implications for economic stability and growth. Persistent increases in the general level of prices not only weaken individuals' purchasing power but also pose serious threats to various sectors of the national economy. Accurate inflation forecasting is therefore of strategic importance for central banks and governments. This paper examines the performance of XGBoost and ARMA models in forecasting inflation during economic shocks and crisis periods in Türkiye. The 1994 economic crisis, 2001 financial crisis, 2008 global financial crisis, and 2018 currency crisis, along with Türkiye's unique macroeconomic conditions, complicate accurate inflation forecasting. This study compares the performance of the XGBoost machine learning algorithm and the ARMA model over different periods, including crises. The findings show XGBoost performs well for large datasets and crisis periods, while ARMA performs better for smaller datasets. Particularly, the forecasting model integrating ARMA's lagged variables into XGBoost proves most effective during crises and across the entire sample period, 1990:02-2024:06. These results highlight the models' sensitivity to data structure and their efficiency in different periods.

Keywords

• Inflation forecast
• XGBoost
• ARMA model

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