Covid-19 Pandemisinin Tarım Fiyatları Üzerindeki Etkisi: Sürekli Dalgacık Dönüşümü Bazlı Granger Nedensellik Testi

Bu çalışmada, korona virüsü pandemisinin spot tarım fiyatları üzerindeki etkisi, hem standart metod hem de dalgacık bazlı korelasyon ve Granger nedensellik testler kullanılarak, incelenmiştir. 22 Ocak – 18 Eylül 2020 dönemine ait günlük ölüm oranı ile mısır, yulaf, kolza, pirinç, soya fasulyesi ve buğday fiyatları ele alınmıştır. Elde edilen test sonuçlarına göre ölüm oranı ile mısır, yulaf, kolza ve soya fasulyesi fiyatları arasında uzun dönemli eşbütünleşme ilişkisinin varlığı tespit edilmiştir. Ayrıca, ölüm oranının mısır ve kolza fiyatlarının uzun dönemde, yulaf fiyatlarının ise hem kısa hem de uzun dönemde Granger nedeni olduğu bulgusuna rastlanmıştır. Diğer taraftan, dalgacık bazlı korelasyon analizi sonuçlarına göre değişkenler arasındaki ilişki zamana göre değişmekte, diğer bir ifadeyle heterojen özellikler sergilemektedir. Dalgacık bazlı nedensellik test bulgularına göre ise, ölüm oranındaki negatif gelişmelerin çoğu tarım fiyatlarındaki negatif gelişmeleri üzerinde istatistiksel olarak anlamlı nedensellik ilişkisine sebep olduğu ortaya çıkmıştır. Elde edilen bulgular, politika yapıcılar için önemli sonuçlar doğurmaktadır.

Anahtar Kelimeler:

Dalgacıklar, Nedensellik, COVID 19, Tarım Emtia Fiyatları, Nedensellik

Impacts of the Covid-19 Pandemic on the Agricultural Prices: New Insights from CWT Granger Causality Test

In this paper, the impacts of the Covid-19 mortality rates on the agricultural spot prices were investigated by using both standard techniques and wavelet-based cohesion and Granger causality tests. Our dataset consisted of daily observations of the mortality rates as well as corn, oats, rapeseed, rice, soybeans, and wheat prices during the period January 22 to September 18, 2020. The findings of the paper revealed that the mortality rate was cointegrated with the prices of corn, oats, rapeseed, and soybeans. Further, the VECM results showed that the mortality rate unidirectionally Granger-caused the corn and rapeseed prices in the long-run, and the oat prices in the short- and long-run. On the other hand, the wavelet cohesion results revealed that the dynamics of the interdependence of the underlying variables were time-varying and heterogeneous over time horizons. The wavelet-based Granger-causality test, however, indicated that the mortality rates negatively caused most of the agricultural prices. These findings yield some important implications for policymakers.

Keywords:

Wavelets Causality, COVID 19, Agricultural Commodity Prices, Wavelets, Causality,

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