Effect of Monetary Indicators on Agricultural Prices: Evidence from Turkiye

Yıl 2023, Cilt: 20 Sayı: 2, 247 - 254, 30.12.2023

Gökhan Çınar

https://doi.org/10.25308/aduziraat.1347590

Öz

This study sought to reveal the effects of Turkish Lira and US dollar exchange rate (EXR), money supply (M2) on agricultural commodity producers’ prices. The direction and the size of the relationship among the data was estimated using VECM Vector Error Correction Model (VECM). The results reveal that the causality runs from M2 to agricultural price (AP) in the short run, but not from AP to M2. In the long run the effect of EXR is more than M2. The coefficient of error correction term in the agricultural price equation is 0.0726 and is statistically significant at 1%. Referring to it, all of the system instability can be adjusted approximately in 14 months. This research shows that the exchange rate (EXR) and money supply (M2) have important long-run effects on agricultural prices (AP). In order to control agricultural prices, it is necessary to follow these macro variables closely.

Anahtar Kelimeler

Agricultural price, monetary policy, food inflation, applied econometrics

Destekleyen Kurum

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Proje Numarası

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Teşekkür

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Parasal Göstergelerin Tarım Fiyatları Üzerindeki Etkisi: Türkiye'den Kanıtlar

Öz

Bu çalışma, Türk Lirası ve ABD doları döviz kurunun (EXR), para arzının (M2) tarımsal emtia üreticilerinin fiyatları üzerindeki etkilerini ortaya koymayı amaçlamıştır. Veriler arasındaki ilişkinin yönü ve boyutu VECM Vektör Hata Düzeltme Modeli (VECM) kullanılarak tahmin edildi. Sonuçlar, nedenselliğin kısa vadede M2'den tarım fiyatına (AP) doğru olduğunu ancak AP'den M2'ye doğru olmadığını ortaya koymaktadır. Uzun vadede EXR'nin etkisi M2'den daha fazladır. Tarım fiyat denkleminde hata düzeltme terimi katsayısı 0,0726 olup istatistiksel olarak %1 düzeyinde anlamlıdır. Buna göre sistem kararsızlıklarının tamamı yaklaşık 14 ayda ayarlanabilmektedir. Bu araştırma, döviz kurunun (EXR) ve para arzının (M2) tarım fiyatları (AP) üzerinde uzun vadeli önemli etkileri olduğunu göstermektedir. Tarım fiyatlarının kontrol edilebilmesi için bu makro değişkenlerin yakından takip edilmesi gerekmektedir.

Anahtar Kelimeler

Tarımsal fiyat, para politikası, gıda enflasyonu, uygulamalı ekonometri

Proje Numarası

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Kaynakça

• Anonymous (2007) International Monetary Fund IMF. Managing Large Capital Inflows; World Economic Outlook, 30p.
• Baek J and Koo W (2010) Analyzing factors affecting US food price inflation. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 58(3): 303-320.
• Baffes J and Haniotis T (2016) What explains agricultural price movements?. Journal of Agricultural Economics, 67(3):706-721.
• Beckmann J, Belke A and Czudaj R (2014) Does global liquidity drive commodity prices?. Journal of Banking & Finance, 48: 224-234.
• Belke A, Bordon I and Hendricks T (2010) Global liquidity and commodity prices–A cointegrated VAR approach for OECD countries. Applied Financial Economics, 20(3): 227-242.
• Belke A, Bordon I and Volz U. (2014) Effects of global liquidity on commodity and food prices. World Development, 44: 31-43.
• Brana S, Djigbenoua M and Prat S (2012) Global excess liquidity and asset prices in emerging countries: A PVAR approach. Emerging Markets Review, 13(3): 256-267. Central Bank of Turkiye (2014) Inflation and price stability, Central Bank of Turkiye, ISBN: 978-605-4911-01-1, 32p.
• Chen P (2015) Global oil prices, macroeconomic fundamentals and China's commodity sector comovements. Energy Policy, 87: 284-294.
• Christopher S, Stock J and Watson M (1990) Inference in linear time series models with some unit roots. Econometrica, 58, 133–144.
• Dickey D and Fuller W (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74: 427-431.
• Dooley M (2000) A model of crises in emerging markets. The economic journal, 110(460): 256-272.
• Enders W (1995) Applied Econometric Time Series. Wiley, New York, USA, pp. 243–251, 376– 377.
• Engle R and Granger C (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 55(2): 251-276.
• Gohin A and François C (2010) The long-run impact of energy prices on world agricultural markets: the role of macro-economic linkages. Energy Policy, 38(1): 333-339.
• Hammoudeh D, Nguyen K and Sousa R (2015) US monetary policy and sectoral commodity prices. Journal of International Money and Finance, 57: 61-85.
• Harri A, Nalley L and Hudson D (2009) The relationship between oil, exchange rates, and commodity prices. Journal of Agricultural and Applied Economics, 41(2): 501-510.
• Hye A and Asghar A (2009) Money Supply, Food Prices and Manufactured Product Prices: A Causality Analysis for Pakistan Economy (No. AIUB-BUS-ECON-2009-03). American International University-Bangladesh (AIUB), Office of Research and Publications (ORP)
• Johansen S and Juselius K (1990) Maximum likelihood estimation and inferences on co-integration with application to the demand for Money. Oxford Bulletin of Economics and Statistics, 52: 169–210.
• Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59: 1551–1580.
• Kang H, Yu B and Yu J (2016) Global Liquidity and Commodity Prices. Review of International Economics, 24(1): 20-36.
• Lombardi M, Osbat C and Schnatz B (2012) Global commodity cycles and linkages: a FAVAR approach. Empirical Economics, 43(2): 651-670.
• MacKinnon J. (1996) Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6): 601–618.
• Mallick S and Sousa R (2013) The real effects of financial stress in the Eurozone. International Review of Financial Analysis, 30:1-17.
• McCalla A (2009) World food prices: causes and consequences. Canadian Journal of Agricultural Economics, 57: 23–34.
• Marques A.C., Fuinhas J.A. and Menegaki A.N., 2014. Interactions between electricity generation sources and economic activity in Greece: A VECM approach. Applied Energy, 132: 34-46.
• Nazlioglu S and Soytas U (2011) World oil prices and agricultural commodity prices: evidence from an emerging market. Energy Economics, 33(3): 488-496.
• Nazlioglu S and Soytas U (2012) Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4): 1098-1104.
• Orkun Oral, İ., Çakıcı, A., Yıldız, F., & Alayoubi, M. (2023). Determinants of food price in Turkey: A Structural VAR approach. Cogent Food & Agriculture, 9(1), 2247169.
• Ratti R and Vespignani J (2015) Commodity prices and BRIC and G3 liquidity: A SFAVEC approach. Journal of Banking & Finance, 53: 18-33.
• Rezitis A (2015) The relationship between agricultural commodity prices, crude oil prices and US dollar exchange rates: a panel VAR approach and causality analysis. International Review of Applied Economics, 29(3): 403-434.
• Sousa J and Zaghini A (2008) Monetary policy shocks in the Euro Area and global liquidity spillovers. International Journal of Finance & Economics, 13(3): 205-218.
• Toda Y and Phillips P (1993) Vector autoregressions and causality. Econometrica, 61(6): 1367-1394.
• Toda Y and Phillips P (1994) Vector autoregression and causality: a theoretical overview and simulation study. Econometric Reviews, 13(2): 259-285.
• Veysel, İ., Şerif, C., & Mustafa, K. (2023). Türkiye'de Gıda Fiyatlarının Belirleyicileri: Fourier Engle-Granger Eşbütünleşme Testi. İktisat Politikası Araştırmaları Dergisi, 10(1): 133-156.
• Yin L and Han L (2016) Macroeconomic impacts on commodity prices: China vs. the United States, Quantitative Finance, 16(3): 489-500