RELATIONSHIP BETWEEN COVID-19 AND MONEY SUPPLY IN TURKEY: EVIDENCE FROM ARDL BOUNDS TESTING APPROACH

Abstract

Purpose- COVID-19 has been a devastating process. During this period, there was a significant increase in the money supply. So, in this process, is there a relationship between COVID-19 and the money supply? This study intends to investigate if COVID-19 and the money supply have both a short- and long-term relationship. Methodology- Logarithmic conversions were used to examine the number of COVID-19 new cases obtained from the Association of Public Health Professionals (HASUDER) and the Turkey Republic Ministry of Health, as well as M2 weekly money supply data from the Central Bank of the Republic of Turkey (CBRT) Electronic Data Distribution System (EVDS). For stationarity tests, the Augmented Dickey-Fuller (ADF), Phillips Perron (PP), and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) unit root tests were used. Due to the different degrees of stationarity of the series, cointegration was not possible, so the long-term relationship was evaluated using Autoregressive Distributed Lag (ARDL). Short-term analyzes included the VAR Model and the Granger Causality Test. Findings- COVID-19 and the money supply, according to the findings, are not cointegrated in the long term. It has been discovered that the series do not move together over the long run. But in the short term, COVID-19 is a Granger cause of the money supply. Conclusion- The increase in COVID-19 cases positively affects the money supply. An increase in the money supply also leads to inflation. Therefore, in order to cope with the inflationary process triggered by the pandemic, measures to prevent the increase in COVID-19 cases are important. These findings will be "confirming" in the design of policies in this process. This study is also a contribution to the literature due to the lack of studies investigating the response of the money supply to COVID-19.

Keywords

• Covid-19
• money supply
• ARDL bounds testing approach
• VAR model
• Granger Causality

References

1. Anser, M. K., Khan, M. A., Zaman, K. Nassani, A. A., Askar, S. E., Abro, M. M. Q., & Kabbani, A. (2021). Financial development during COVID-19 pandemic: the role of Coronavirus testing and Functional labs. Financial Innovation, 7(1), 1-13. doi: 10.1186/s40854-021-00226-4
2. Buchholz, C. (2020). The Coronavirus and carceral capitalism. Developing economics. https://developingeconomics.org/2020/04/11/thecoronavirus-and-carceral-capitalism/
3. CBRT. (2013). Bulletin No.31. https://www.tcmb.gov.tr/wps/wcm/connect/23fa999c-481e-478b-ba87- 9f61951ca8e7/Bulten31.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-23fa999c-481e-478b-ba87-9f61951ca8e7-m3fB9EG
4. Çiğdem, G. (2020). A Double-edged sword: COVID-19 and emission: proofs from Turkey. Eurasian Journal of Researches in Social and Economics (EJRSE), 7(7), 1-12. Çil Yavuz, N. (2015). Finansal ekonometri. İkinci Basım. İstanbul: Der Yayınları
5. Dickey, D. A. & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072.
6. Eğilmez, M. (2018). Para arzı nedir ve nasıl ölçülür? https://www.mahfiegilmez.com/2018/07/para-arz-nedir-ve-nasl-olculur.html
7. Elgin, C., Basbug, G. & Yalaman, A. (2020). Economic policy responses to a pandemic: developing the Covid-19 economic stimulus index. Centre for Economic Policy Research. https://voxeu.org/article/economic-policy-responses-pandemic-covid-19-economic-stimulus-index
8. Enders, W. (1995). Applied econometric time series. New York: Wiley.

9. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438. doi:10.2307/1912791
10. Granger, C. W. J. (1980). Testing for causality: a personal viewpoint. Journal of Economic Dynamics and Control, 2, 329-352.
11. Granger, C. W. J. (1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 16(1), 121-130.
12. Keating, J. W. (1990). Identifying VAR models under rational expectations. Journal of Monetary Economics, 25(3), 453-476.
13. Kumar, V., Leone, R. P., & Gaskins, J. N. (1995). Aggregate and disaggregate sector forecasting using consumer confidence measures. International Journal of Forecasting, 11(3), 361-377.
14. Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159-178.
15. Mousina, D. (2020). Econosight: inflation in the COVID-19 world, https://www.ampcapital.com/americas/en/insightshub/articles/2020/july/inflation-in-the-covid-19-world
16. Orhan, O. Z. & Erdoğan, S. (2002). Para politikası. İstanbul: Avcılar Ofset.
17. Parasız, İ. (2003). Para politikası. 6. Basım. Bursa: Ezgi Kitabevi.
18. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.
19. Phillips, P. C. B. & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
20. Roubini, N. (2020). Coronavirus pandemic has delivered the fastest, deepest economic shock in history. The Guardian. https://www.theguardian.com/business/2020/mar/25/coronavirus-pandemic-has-delivered-the-fastest-deepest-economic-shock-in-history
21. Saad-Filho, A. (2020). From COVID-19 to the end of Neoliberalism. Critical Sociology, SAGE Journals, 46(4-5), 477-485. https://doi.org/10.1177%2F0896920520929966
22. Saito, J. (2021). COVID-19 and money supply. Japan Center for Economic Research. https://www.jcer.or.jp/english/covid-19-and-money-supply
23. Schwert, G. W. (1989). Tests for unit root: a Monte Carlo investigation. Journal of Business and Economic Statistics, 7(2), 147-160.
24. Sims, C. A. (1972). Money, income, and causality. The American Economic Review, 62(4), 540-552.
25. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.
26. Tarı, R. (2006). Ekonometri. 4. Baskı. İstanbul: Avcı Ofset.
27. Taymaz, E. (2020). COVID-19 tedbirlerinin Türkiye Ekonomisine etkisi ve çözüm önerileri. Sarkaç. https://sarkac.org/2020/04/covid19- tedbirlerinin-turkiyeekonomisine-etkisi-cozum-onerileri/
28. Ugarteche, O. & Ocampo, A. (2020). COVID-19: the beginning of domino effect. Observatorio Economico Latinoamericano, OBELO Discussion Paper. http://www.networkideas.org/wp-content/uploads/2020/04/covid19_domino_effect.pdf
29. Voyvoda, E. & Yeldan, E. A. (2020). COVID-19 salgınının Türkiye Ekonomisi üzerine etkileri ve politika alternatiflerinin makroekonomik genel denge analizi, https://yeldane.files.wordpress.com/2020/05/covid_voyvodayeldan_v2_31mayis.pdf
30. WHO. Coronavirus (COVID-19) dashboard, https://covid19.who.int/?gclid=CjwKCAjw07qDBhBxEiwA6pPbHicJAt5BTH7UFUqyiqQZUWTqMNNq0d7KJweiIu3NNJ8QAmhVzvw3hoCuKsQAvD_BwE
31. WHO. (2020). Archived: WHO timeline-COVID-19, https://www.who.int/news-room/detail/27-04-2020-whotimeline---covid- 19?gclid=EAIaIQobChMIotiQ-rfh6gIVCIBQBh2Ciwm_EAAYASAAEgIimfD_BwE