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

Yıl 2020, Cilt: 5 Sayı: Özel Sayı, 76 - 96, 26.12.2020
https://doi.org/10.30784/epfad.810558

Öz

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.

Kaynakça

  • Akhtaruzzaman, M., Boubaker, S. and Sensoy, A. (2020). Financial contagion during Covid–19 crisis. Finance Research Letters, 38, 1–20. https://doi.org/10.1016/j.frl.2020.101604
  • Alam, M. S., Shahzad, S. J. H. and Ferrer, R. (2019). Causal flows between oil and forex markets using high-frequency data: Asymmetries from good and bad volatility. Energy Economics, 84, 1–21. https://doi.org/10.1016/j.eneco.2019.104513
  • Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S. and Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID-19 pandemic. Technological Forecasting and Social Change, 161, 1–12. https://doi.org/10.1016/j.techfore.2020.120261
  • Atkeson, A. (2020). What will be the economic impact of Covid-19 in the US? Rough estimates of disease scenarios (NBER Working Paper No 26867). Retrieved from http://acdc2007.free.fr/nber26867.pdf
  • Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C. and Viratyosin, T. (2020). The unprecedented stock market impact of COVID-19 (NBER Working Paper No 26945). Retrieved from https://www.nber.org/papers/w26945
  • Brewin, D. G. (2020). The impact of COVID‐19 on the grains and oilseeds sector. Canadian Journal of Agricultural Economics, 68(2), 185–188. https://doi.org/10.1111/cjag.12239
  • Conlon, T. and McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the Covid-19 bear market. Finance Research Letters, 35, 1–5. https://doi.org/10.1016/j.frl.2020.101607
  • Dutta, A., Das, D., Jana, R. K. and Vo, X. V. (2020). COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin. Resources Policy, 69, 1–6. https://doi.org/10.1016/j.resourpol.2020.101816
  • Fernandes, N. (2020). Economic effects of coronavirus outbreak (COVID-19) on the world economy (IESE Business School Working Paper No. WP-1240-E). Retrieved from https://foroparalapazenelmediterraneo.es/wp-content/uploads/2020/03/SSRN-id3557504.pdf.pdf
  • Gençay, R., Selçuk, F. and Whitcher, B. J. (2001). An introduction to wavelets and other filtering methods in finance and economics. San Diego: Academic Press (Elsevier).
  • Ghazanfari, A. (2020). The impact of the Covid-19 pandemic and crude oil price crisis on the price of automobile fuels in European countries. Diverse Journal of Multidisciplinary Research, 2(6), 10–19. Retrieved from https://diverseresearchjournals.com/
  • Gherghina, Ș. C., Armeanu, D. Ș. and Joldeș, C. C. (2020). Stock market reactions to Covid-19 pandemic outbreak: quantitative evidence from ARDL bounds tests and Granger causality analysis. International Journal of Environmental Research and Public Health, 17(18), 1–35. https://doi.org/10.3390/ijerph17186729
  • Goodell, J. W. and Goutte, S. (2020). Co-movement of Covid-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 1–6. https://doi.org/10.1016/j.frl.2020.101625
  • Grinsted, A., Moore, J. C. and Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), 561–566. Retrieved from https://hal.archives-ouvertes.fr
  • Gupta, M., Abdelmaksoud, A., Jafferany, M., Lotti, T., Sadoughifar, R. and Goldust, M. (2020). Covid‐19 and economy [Special issue]. Dermatologic Therapy, 33(5). http://dx.doi.org/10.1111/dth.13329
  • Hatemi-J, A. (2008). Tests for cointegration with two unknown regime shifts with an application to financial market integration. Empirical Economics, 35(3), 497–505. https://doi.org/10.1007/s00181-007-0175-9
  • Ji, Q., Zhang, D. and Zhao, Y. (2020). Searching for safe-haven assets during the Covid-19 pandemic. International Review of Financial Analysis, 71(2020), 1–10. https://doi.org/10.1016/j.irfa.2020.101526
  • Jun, W., Mahmood, H. and Zakaria, M. (2020). Impact of trade openness on environment in China. Journal of Business Economics and Management, 21(4), 1185–1202. https://doi.org/10.3846/jbem.2020.12050
  • Kang, S. H., Tiwari, A. K., Albulescu, C. T. and Yoon, S. M. (2019). Time-frequency co-movements between the largest nonferrous metal futures markets. Resources Policy, 61, 393–398. https://doi.org/10.1016/j.resourpol.2017.12.010
  • Kara, E. and Diken, A. (2020). Climatic change: The effect of rainfall on economic growth. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 11(28), 665–679. https://doi.org/10.21076/vizyoner.693363
  • Kerr, A. K. (2020). The Covid‐19 pandemic and agriculture: Short‐ and long‐run implications for international trade relations. Canadian Journal of Agricultural Economics, 68(2), 225–229. https://doi.org/10.1111/cjag.12230
  • Lahmiri, S. and Bekiros, S. (2020). The impact of Covid-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets. Chaos, Solitons & Fractals, 138(2020), 1–6. https://doi.org/10.1016/j.chaos.2020.109936
  • Lawley, C. (2020). Potential impacts of COVID‐19 on Canadian farmland markets. Canadian Journal of Agricultural Economics, 68(2), 245–250. https://doi.org/10.1111/cjag.12242
  • Lee, J. and Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089. https://doi.org/10.1162/003465303772815961
  • Mensi, W., Sensoy, A., Vo, X. V. and Kang, S. H. (2020). Impact of Covid-19 outbreak on asymmetric multifractality of gold and oil prices. Resources Policy, 69(2020), 1–11. https://doi.org/10.1016/j.resourpol.2020.101829
  • Narayan, P. K., Devpura, N. and Hua, W. (2020). Japanese currency and stock market—What happened during the Covid-19 pandemic? Economic Analysis and Policy, 68, 191–198. https://doi.org/10.1016/j.eap.2020.09.014
  • Olayeni, O. R. (2016). Causality in continuous wavelet transform without spectral matrix factorization: theory and application. Computational Economics, 47(3), 321–340. https://doi.org/10.1007/s10614-015-9489-4
  • Öztürk, Ö., Şişman, M. Y., Uslu, H. and Çıtak, F. (2020). Effect of Covid-19 outbreak on Turkish stock market: A sectoral-level analysis. Hitit University Journal of Social Sciences Institute, 13(1), 56–68. https://doi.org/10.17218/hititsosbil.728146
  • Percival, D. B. and Walden, A. T. (2000). Wavelet methods for time series analysis (Vol. 4). Cambridge: Cambridge University Press.
  • Pu, M. and Zhong, Y. (2020). Rising concerns over agricultural production as Covid-19 spreads: Lessons from China. Global Food Security, 26(2020), 1–7. https://doi.org/10.1016/j.gfs.2020.100409
  • Rawal, V., Kumar, M., Verma, A. and Pais, J. (2020). Covid-19 Lockdown: Impact on agriculture and the rural economy (Society for Social and Economic Research Working Paper No.S3/209). Retrieved from http://archive.indianstatistics.org/sserwp/sserwp2003.pdf
  • Rua, A. (2010). Measuring comovement in the time-frequency space. Journal of Macroeconomics, 32(2), 685–691. https://doi.org/10.1016/j.jmacro.2009.12.005
  • Salisu, A. A., Ebuh, G. U. and Usman, N. (2020). Revisiting oil-stock nexus during Covid-19 pandemic: Some preliminary results. International Review of Economics & Finance, 69, 280–294. https://doi.org/10.1016/j.iref.2020.06.023
  • Sari, S. S. and Kartal, T. (2020). Covid-19 salgınının altın fiyatları, petrol fiyatları ve VIX endeksi ile arasındaki ilişki [The relationship of Covid-19 pandemic with gold prices, oil prices and VIX index]. Erzincan Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 13(1), 93–109. https://doi.org/10.46790/erzisosbil.748181
  • Şenol, Z. and Zeren, F. (2020). Coronavirus (Covid-19) and stock markets: The effects of the pandemic on the global economy. Eurasian Journal of Researches in Social and Economics 7(4), 1–16. Retrieved from https://dergipark.org.tr/en/pub/asead
  • Siche, R. (2020). What is the impact of Covid-19 disease on agriculture?. ScientiaAgropecuaria, 11(1), 3–6. http://dx.doi.org/10.17268/sci.agropecu.2020.01.00
  • Tiwari, A. K., Olayeni, R. O., Olofin, S. A. and Chang, T. (2019). The Indian inflation–growth relationship revisited: robust evidence from time–frequency analysis. Applied Economics, 51(51), 5559–5576. https://doi.org/10.1080/00036846.2019.1616065
  • Topcu, M. and Gulal, O. S. (2020). The impact of Covid-19 on emerging stock markets. Finance Research Letters, 36(2020), 1–4. https://doi.org/10.1016/j.frl.2020.101691
  • Torrence, C. and Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American meteorological society, 79(1), 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
  • Torun, E. and Demireli, E. (2019). Sürekli dalgacık dönüşümlü Granger nedensellik analizi: Türkiye örneği [Nonparametrıc continuous wavelet transform (CWT) Granger causality analysis: the case of Turkey]. Çanakkale Onsekiz Mart Üniversitesi Yönetim Bilimleri Dergisi, 17(34), 389–487. doi:10.35408/comuybd.502454
  • Wang, J., Shao, W. and Kim, J. (2020). Analysis of the impact of Covid-19 on the correlations between crude oil and agricultural futures. Chaos, Solitons & Fractals, 136(2020), 1–7. https://doi.org/10.1016/j.chaos.2020.109896
  • Yang, Y., Zhang, H. and Chen, X. (2020). Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. Annals of Tourism Research, 83, 1–6. http://dx.doi.org/10.1016/j.annals.2020.102913

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

Yıl 2020, Cilt: 5 Sayı: Özel Sayı, 76 - 96, 26.12.2020
https://doi.org/10.30784/epfad.810558

Öz

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.

Kaynakça

  • Akhtaruzzaman, M., Boubaker, S. and Sensoy, A. (2020). Financial contagion during Covid–19 crisis. Finance Research Letters, 38, 1–20. https://doi.org/10.1016/j.frl.2020.101604
  • Alam, M. S., Shahzad, S. J. H. and Ferrer, R. (2019). Causal flows between oil and forex markets using high-frequency data: Asymmetries from good and bad volatility. Energy Economics, 84, 1–21. https://doi.org/10.1016/j.eneco.2019.104513
  • Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S. and Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID-19 pandemic. Technological Forecasting and Social Change, 161, 1–12. https://doi.org/10.1016/j.techfore.2020.120261
  • Atkeson, A. (2020). What will be the economic impact of Covid-19 in the US? Rough estimates of disease scenarios (NBER Working Paper No 26867). Retrieved from http://acdc2007.free.fr/nber26867.pdf
  • Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C. and Viratyosin, T. (2020). The unprecedented stock market impact of COVID-19 (NBER Working Paper No 26945). Retrieved from https://www.nber.org/papers/w26945
  • Brewin, D. G. (2020). The impact of COVID‐19 on the grains and oilseeds sector. Canadian Journal of Agricultural Economics, 68(2), 185–188. https://doi.org/10.1111/cjag.12239
  • Conlon, T. and McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the Covid-19 bear market. Finance Research Letters, 35, 1–5. https://doi.org/10.1016/j.frl.2020.101607
  • Dutta, A., Das, D., Jana, R. K. and Vo, X. V. (2020). COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin. Resources Policy, 69, 1–6. https://doi.org/10.1016/j.resourpol.2020.101816
  • Fernandes, N. (2020). Economic effects of coronavirus outbreak (COVID-19) on the world economy (IESE Business School Working Paper No. WP-1240-E). Retrieved from https://foroparalapazenelmediterraneo.es/wp-content/uploads/2020/03/SSRN-id3557504.pdf.pdf
  • Gençay, R., Selçuk, F. and Whitcher, B. J. (2001). An introduction to wavelets and other filtering methods in finance and economics. San Diego: Academic Press (Elsevier).
  • Ghazanfari, A. (2020). The impact of the Covid-19 pandemic and crude oil price crisis on the price of automobile fuels in European countries. Diverse Journal of Multidisciplinary Research, 2(6), 10–19. Retrieved from https://diverseresearchjournals.com/
  • Gherghina, Ș. C., Armeanu, D. Ș. and Joldeș, C. C. (2020). Stock market reactions to Covid-19 pandemic outbreak: quantitative evidence from ARDL bounds tests and Granger causality analysis. International Journal of Environmental Research and Public Health, 17(18), 1–35. https://doi.org/10.3390/ijerph17186729
  • Goodell, J. W. and Goutte, S. (2020). Co-movement of Covid-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 1–6. https://doi.org/10.1016/j.frl.2020.101625
  • Grinsted, A., Moore, J. C. and Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), 561–566. Retrieved from https://hal.archives-ouvertes.fr
  • Gupta, M., Abdelmaksoud, A., Jafferany, M., Lotti, T., Sadoughifar, R. and Goldust, M. (2020). Covid‐19 and economy [Special issue]. Dermatologic Therapy, 33(5). http://dx.doi.org/10.1111/dth.13329
  • Hatemi-J, A. (2008). Tests for cointegration with two unknown regime shifts with an application to financial market integration. Empirical Economics, 35(3), 497–505. https://doi.org/10.1007/s00181-007-0175-9
  • Ji, Q., Zhang, D. and Zhao, Y. (2020). Searching for safe-haven assets during the Covid-19 pandemic. International Review of Financial Analysis, 71(2020), 1–10. https://doi.org/10.1016/j.irfa.2020.101526
  • Jun, W., Mahmood, H. and Zakaria, M. (2020). Impact of trade openness on environment in China. Journal of Business Economics and Management, 21(4), 1185–1202. https://doi.org/10.3846/jbem.2020.12050
  • Kang, S. H., Tiwari, A. K., Albulescu, C. T. and Yoon, S. M. (2019). Time-frequency co-movements between the largest nonferrous metal futures markets. Resources Policy, 61, 393–398. https://doi.org/10.1016/j.resourpol.2017.12.010
  • Kara, E. and Diken, A. (2020). Climatic change: The effect of rainfall on economic growth. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 11(28), 665–679. https://doi.org/10.21076/vizyoner.693363
  • Kerr, A. K. (2020). The Covid‐19 pandemic and agriculture: Short‐ and long‐run implications for international trade relations. Canadian Journal of Agricultural Economics, 68(2), 225–229. https://doi.org/10.1111/cjag.12230
  • Lahmiri, S. and Bekiros, S. (2020). The impact of Covid-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets. Chaos, Solitons & Fractals, 138(2020), 1–6. https://doi.org/10.1016/j.chaos.2020.109936
  • Lawley, C. (2020). Potential impacts of COVID‐19 on Canadian farmland markets. Canadian Journal of Agricultural Economics, 68(2), 245–250. https://doi.org/10.1111/cjag.12242
  • Lee, J. and Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089. https://doi.org/10.1162/003465303772815961
  • Mensi, W., Sensoy, A., Vo, X. V. and Kang, S. H. (2020). Impact of Covid-19 outbreak on asymmetric multifractality of gold and oil prices. Resources Policy, 69(2020), 1–11. https://doi.org/10.1016/j.resourpol.2020.101829
  • Narayan, P. K., Devpura, N. and Hua, W. (2020). Japanese currency and stock market—What happened during the Covid-19 pandemic? Economic Analysis and Policy, 68, 191–198. https://doi.org/10.1016/j.eap.2020.09.014
  • Olayeni, O. R. (2016). Causality in continuous wavelet transform without spectral matrix factorization: theory and application. Computational Economics, 47(3), 321–340. https://doi.org/10.1007/s10614-015-9489-4
  • Öztürk, Ö., Şişman, M. Y., Uslu, H. and Çıtak, F. (2020). Effect of Covid-19 outbreak on Turkish stock market: A sectoral-level analysis. Hitit University Journal of Social Sciences Institute, 13(1), 56–68. https://doi.org/10.17218/hititsosbil.728146
  • Percival, D. B. and Walden, A. T. (2000). Wavelet methods for time series analysis (Vol. 4). Cambridge: Cambridge University Press.
  • Pu, M. and Zhong, Y. (2020). Rising concerns over agricultural production as Covid-19 spreads: Lessons from China. Global Food Security, 26(2020), 1–7. https://doi.org/10.1016/j.gfs.2020.100409
  • Rawal, V., Kumar, M., Verma, A. and Pais, J. (2020). Covid-19 Lockdown: Impact on agriculture and the rural economy (Society for Social and Economic Research Working Paper No.S3/209). Retrieved from http://archive.indianstatistics.org/sserwp/sserwp2003.pdf
  • Rua, A. (2010). Measuring comovement in the time-frequency space. Journal of Macroeconomics, 32(2), 685–691. https://doi.org/10.1016/j.jmacro.2009.12.005
  • Salisu, A. A., Ebuh, G. U. and Usman, N. (2020). Revisiting oil-stock nexus during Covid-19 pandemic: Some preliminary results. International Review of Economics & Finance, 69, 280–294. https://doi.org/10.1016/j.iref.2020.06.023
  • Sari, S. S. and Kartal, T. (2020). Covid-19 salgınının altın fiyatları, petrol fiyatları ve VIX endeksi ile arasındaki ilişki [The relationship of Covid-19 pandemic with gold prices, oil prices and VIX index]. Erzincan Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 13(1), 93–109. https://doi.org/10.46790/erzisosbil.748181
  • Şenol, Z. and Zeren, F. (2020). Coronavirus (Covid-19) and stock markets: The effects of the pandemic on the global economy. Eurasian Journal of Researches in Social and Economics 7(4), 1–16. Retrieved from https://dergipark.org.tr/en/pub/asead
  • Siche, R. (2020). What is the impact of Covid-19 disease on agriculture?. ScientiaAgropecuaria, 11(1), 3–6. http://dx.doi.org/10.17268/sci.agropecu.2020.01.00
  • Tiwari, A. K., Olayeni, R. O., Olofin, S. A. and Chang, T. (2019). The Indian inflation–growth relationship revisited: robust evidence from time–frequency analysis. Applied Economics, 51(51), 5559–5576. https://doi.org/10.1080/00036846.2019.1616065
  • Topcu, M. and Gulal, O. S. (2020). The impact of Covid-19 on emerging stock markets. Finance Research Letters, 36(2020), 1–4. https://doi.org/10.1016/j.frl.2020.101691
  • Torrence, C. and Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American meteorological society, 79(1), 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
  • Torun, E. and Demireli, E. (2019). Sürekli dalgacık dönüşümlü Granger nedensellik analizi: Türkiye örneği [Nonparametrıc continuous wavelet transform (CWT) Granger causality analysis: the case of Turkey]. Çanakkale Onsekiz Mart Üniversitesi Yönetim Bilimleri Dergisi, 17(34), 389–487. doi:10.35408/comuybd.502454
  • Wang, J., Shao, W. and Kim, J. (2020). Analysis of the impact of Covid-19 on the correlations between crude oil and agricultural futures. Chaos, Solitons & Fractals, 136(2020), 1–7. https://doi.org/10.1016/j.chaos.2020.109896
  • Yang, Y., Zhang, H. and Chen, X. (2020). Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. Annals of Tourism Research, 83, 1–6. http://dx.doi.org/10.1016/j.annals.2020.102913
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finans
Bölüm Makaleler
Yazarlar

Remzi Gök 0000-0002-9216-5210

Erkan Kara 0000-0001-7228-0396

Yayımlanma Tarihi 26 Aralık 2020
Kabul Tarihi 1 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 5 Sayı: Özel Sayı

Kaynak Göster

APA Gök, R., & Kara, E. (2020). Impacts of the Covid-19 Pandemic on the Agricultural Prices: New Insights from CWT Granger Causality Test. Ekonomi Politika Ve Finans Araştırmaları Dergisi, 5(Özel Sayı), 76-96. https://doi.org/10.30784/epfad.810558