What can we learn from Divisia and simple-sum monetary indexes? Structural stability in frequency domain from time-frequency analysis
Yinan Tang, Ping Chen
China Institute, Fudan University, Shanghai, China
Please cite the paper as:
Yinan Tang, Ping Chen, (2018), What can we learn from Divisia and simple-sum monetary indexes? Structural stability in frequency domain from time-frequency analysis, World Economics Association (WEA) Conferences, No. 1 2018, Monetary Policy after the Global Crisis, 19th February to 20th April, 2018
Abstract
What can we know from different monetary indexes, such as simple-sum and DIvisia index? What information in monetary policy analysis. In 1985- 1988, we discovered empirical and theoretical evidence of economic chaos from monetary indexes M2, DDM2, DDM3, DML, and DSM2. The existence of monetary chaos implies that monetary dynamics is endogenous rather than exogenous mechanism in nature. After 2009 financial crisis, we analyzed cyclic pattern of monetary indexes through HP filter by means of time-frequency (TF) analysis, which is a non-parametric approach in non-stationary time series analysis. We found out that MB (monetary base) did reveal the short-term effect of monetary policy during crisis period (2007-2015), but other indexes, including DM4T, DM4T-, DM3, M2, and M2M from CFS (Center for Financial Stability), show remarkable resilience of frequency pattern in time. This is a further evidence of nonlinear and coherent nature of monetary dynamics that is beyond the scope of the Frisch model of noise-driven business cycles in macro econometrics. Its implication in monetary policy is still an open issue for future study.
The paper brought at the same table very important economic theories. Excellent methodology and idea! However, I think that you should improve interpretation of the results and to offer wider discussion of it.
Since the economy is not isolated from the weather, which is chaotic, it is reasonable to assume that chaos exists in most economic data. But since economic data are very noisy, it can be difficult to detect the chaotic signal under the white noise. The ability to detect the chaos reflects well on the quality of the data and their high signal to noise ratios. Determining the source of the chaos and the information content in the chaos are potential topics for challenging research. Continuing advances in computer technology may eventually provide numerical methods for extracting such deep information from the fractal attractor set characterizing chaotic dynamics.