Bütçe Tahmincileri için Enflasyon Tahmini: Türkiye'de YSA Modeli Performansının İncelenmesi

Öz

Bütçe gelir ve harcama tahminlerinin güvenilirliği, enflasyon tahminlerinin doğruluğuna bağlıdır. Gerçekçi enflasyon tahminleri olmadan sağlıklı bir bütçe tahmini yapmak mümkün değildir. Bu çalışma, Türkiye’de bütçe tahmincilerine enflasyon tahminleri konusunda rehberlik etmek amacıyla hazırlanmıştır. Çalışmada, 2005-2023 dönemine ait veriler kullanılmıştır. Döviz kuru sepeti (Dolar ve Euro), işsizlik, ithalat, ihracat, bütçe gelir ve harcamaları, faiz oranı, sanayi üretim endeksi, para arzı, genel fiyat endeksi ve asgari ücret, Holt-Winters, ARIMA, SARIMA, Prophet, LSTM ve Hibrit yöntemleriyle tahmin edilmiştir. Daha sonra bu tahminler kullanılarak YSA, SVR, RF ve GBM yöntemiyle aylık enflasyon tahminleri üretilmiştir. Sonuçlar, çalışmada YSA ile edilen tahminlerin, Türkiye’nin bütçe kanunu ve orta vadeli programda yer alan enflasyon tahminlerinden daha gerçekçi olduğunu göstermektedir. Çalışma, YSA yönteminin bütçe tahmincileri tarafından enflasyonu doğru tahmin etmek için etkili bir araç olarak kullanılabileceğini ortaya koymaktadır. Bulgular, IMF, OECD, Merkez Bankası ve Avrupa Birliği tahminleriyle karşılaştırmalı analiz yoluyla değerlendirilmiştir. Gelecek akademik araştırmaları desteklemek amacıyla, 2025 yılına ilişkin bağımsız değişkenler ve enflasyon tahminleri de çalışmaya dahil edilmiştir.

Anahtar Kelimeler

• Bütçe Tahmini
• Enflasyon Tahmini
• Yapay Sinir Ağları

Forecasting the Inflation for Budget Forecasters: An Analysis of ANN Model Performance in Türkiye

Öz

The reliability of budget revenue and expenditure forecasts depends on the accuracy of inflation forecasts. Without realistic inflation forecasts, it is not possible to produce sound budget forecasts. This study aims to guide budget forecasters in Türkiye by providing accurate inflation forecasts. The analysis utilizes data from the 2005–2023 period. The basket exchange rate (USD and Euro), unemployment, imports, exports, budget revenues and expenditures, interest rates, industrial production index, money supply, general price index, and minimum wage are forecasted using Holt-Winters, ARIMA, SARIMA, Prophet, LSTM, and Hybrid models. These forecasts are then used as inputs in ANN, SVR, RF, and GBM models to forecast monthly inflation. The results indicate that the forecasts generated with ANN are significantly more realistic than those presented in Türkiye’s budget law and the Medium-Term Program. The study demonstrates that ANN can be an effective tool for budget forecasters in accurately forecasting inflation and, consequently, improving budget forecasts. The findings are further evaluated through a comparative analysis with forecasts from institutions such as the IMF, OECD, Central Bank, and the European Union. To support future academic research, inflation forecasts for 2025, along with forecasts for independent variables, are also included in the study.

Anahtar Kelimeler

• Budget Forecasting
• Inflation Forecasting
• Artificial Neural Network

Kaynakça

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