Understanding the time-frequency dynamics of money demand, oil prices and macroeconomic variables: The case of India

This study investigates the multi-scale lead-lag nexuses between money demand and each of real GDP, interest rate, exchange rate, oil prices, inflation-defining CPI for the third largest global oil consumer, India, using the monthly data ranging from 1994 M1 to 2017 M11. A special focus is placed on the effect of changes in oil prices and inflation-defining CPI on money demand. The paper uses the wavelet coherency and the partial wavelet coherency techniques to achieve the goals. The univariate empirical analysis reveals that during the whole sample period, the demand for money, GDP, interest rate, and exchange rate show the same pattern in terms of wavelet power spectra, suggesting weak volatility levels across the time-frequency plane. The bivariate analysis indicates that the partial wavelet coherent empirical results underscore the presence of either a unidirectional or a bidirectional causal relationship between money demand and the underlining oil and macroeconomic variables. In particular, in terms of the wavelet coherency results, money demand exhibits the greatest interdependency with real GDP across the time-frequency domain, underscoring the importance of the transaction demand for money, while it has a much lower interdependency with interest rate, exchange rate and oil prices, giving lower importance to speculative demand for money and monetary stability perhaps due to financial innovations.