The Application of Continuous Wavelet Transform in Discovering the Dynamics of the Causal Relationship between Liquidity and its Components with Inflation: a Case Study of Iran

Document Type : Research Paper


1 Associate Professor, University of Mazandaran,

2 Phd Student Economics/ University of Mazandaran


This paper provides a fresh new insight into the relationship between Liquidity and its Components with inflation in Iran applying Continuous Wavelet Transform and Frequency-Time Domain Analysis. According to the results: (1) in the long-run, there is a stable, strong and in-phase relationship between money growth and inflation so that an increase in the money growth with a lag about 2.5 years leads to an increase in inflation. (2) In the short-run and two-month scale, the increase (decrease) in quasi-money growth is accompanied by a decrease (increase) in inflation. However, in the medium-run and long-run, inflation leads to quasi-monetary growth pointing out the fact that these variables are anti-phase in the medium-run and in-phase in the long-run. The relationship between liquidity growth and inflation in the short-run and medium-run is heterogeneous and depends on the relationship between liquidity components and inflation. In the long-run, liquidity growth follows the inflation and it will be affected after 2 years.
JEL Classification: C49, E31, E51


Main Subjects

  1. سحابی، بهرام، سلیمانی، سیروس، خضری، سمیه و خضری، محسن (1392). اثرات رشد نقدینگی بر تورم در اقتصاد ایران: مدل‌های تغییر رژیم. راهبرد اقتصادی. 2(4)، 146-121.
  2. طیب­نیا، علی و تقی ­ملایی، سعید (1389). پول و تورم در ایران رویکرد خودرگرسیونی برداری (VAR). دو فصلنامه‌ی برنامه و بودجه. 15(1)، 29-3.
  3. کاکویی، نصیبه و نقدی، یزدان (1393). رابطه­­ی پول و تورم در ایران: شواهدی براساس مدل P*. فصلنامه‌ی پژوهش‌های اقتصادی (رشد و توسعه‌ی پایدار). 14(2)، 156-135.
  4. مرکز پژوهش‌های شورای اسلامی (1394). درآمدی بر مبادی نظری درون‌زایی پول و دلالت­های سیاستی آن برای اقتصاد ایران. تهران: عادل پیغامی.
    1. Aguiar-Conraria L., & Soares M.J. (2011). The Continuous Wavelet Transform: A Primer. NIPE Working Paper Series.
    2. Aguiar-Conraria, L., Azevedo, N., & Soares, M.J. (2008). Using wavelets to decompose the time-frequency effects of monetary policy. Physica A: Statistical Mechanics and its Applications, 387: 2863–2878.
    3. Atrkar Roshan, S. (2014). Inflation and Money supply growth in Iran: Empirical Evidences from Cointegration and Causality. Iranian Economic Review. 18(1): 131-152.
    4. Bekiros. S., T. Muzaffar, A., S. Uddin, G. & Vidal-García, J. (2017). Money supply and infllation dynamics in the Asia-Pacific economies: a time-frequency approach. Studies in Nonlinear Dynamics & Econometrics, 21(3): 1-12.
    5. Daubechies, I. (1992). Ten lectures on wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics, 61, Philadelphia: SIAM.
    6. Friedman, M. (1956). The quantity theory of money: A restatement. In M. Friedman (Ed.), Studies in the Quantity Theory of Money (pp. 3–21). Chicago: University of Chicago Press.
    7. Fisher, I. (1920). The Purchasin Power of Money. New York: Macmillan.
    8. Goupillaud, P., Grossman, A., & Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration, 23: 85–102.
    9. Jaeger, A. (2003). The ECB’s Money Pillar: An Assessment. International Monetary Fund, Working Paper No. 82.
    10. Jiang, C., & T. Chang, X. L. Li. ) 2015(. Money Growth and Infllation in China: New Evidence from a Wavelet Analysis. International Review of Economics and Finance, 35: 249–261.
    11. Johnson, H. (1978). Selected essays In Monetary Economics (First ed.). London: George Allen Unwin.
    12. Loh, L. (2013). Co-movement of Asia–Pacific with European and US stock market returns: a cross-time-frequency analysis. Research in International Business and Finance, 29: 1–13.
    13. Lucas, R. (1980). Two illustrations of the quantity theory of money. American Economic Review, 70: 1005–1014.
    14. McCallum, B. T., & Nelson, E. (2010). Money and Inflation: Some Critical Issues, Handbook of Monetary Economics, 3: 97-153.
    15. Roueff, F., & Sachs, R. (2011). Locally stationary long memory estimation. Stochastic Processes and their Applications, 121(4): 813–844.
    16. Rua, A., & Nunes, L.C. (2009). International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16: 632-639.
    17. Rua, A. (2012). Money growth and inflation in the Euro area: A time-frequency view. Oxford Bulletin of Economics and Statistics, 74(6): 875–885.
    18. Shahbaz  M., Tiwari A.K., & Tahir M.I. (2012). Does CPI Granger-Cause WPI? New Extensions from Frequency Domain Approach in Pakistan. Journal  of Economic Modelling. 29: 1592–1597.
    19. Tiwari, A.K., Mutascu, M., & Andries, A.M. (2013). Decomposing time-frequency relationship between producer price and consumer price indices in Romania through wavelet analysis. Economic Modelling, 31: 151–159.
    20. Tiwari A.K., Surecsh  K.G., Arouri M., & Teulon F.(2014). Causality Between Consumer Price and Producer Price: Evidence from Mexico. Journal of Economic Modelling. 36: 432–440.
    21. Torrence, C., & Compo, G. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79: 61–78.
    22. Torrence, C., & Webster P. J. (1998). The annual cycle of persistence in the El Niño–Southern Oscillation. Quarterly Journal of the Royal Meteorological Society, 124: 1985–2004.
    23. Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics, 34: 241–247.
    24. Wen, Y. (2005). Understanding the inventory cycle. Journal of Monetary Economics. 52(8): 1533-1555.