The dilemma between difficulties inmodeling and lack of economic meaning can be solved by an
objective data analysis method, i.e. Empirical Mode Decomposition (EMD), introduced by Huang
et al. (1998). EMDis an empirical, intuitive, direct and self-adaptive data processingmethod which is
proposed especially for nonlinear and non-stationary data. The core of EMD is to decompose data
into a small number of independent and nearly periodic intrinsic modes based on local characteristic
scale, which is defined as the distance between two successive local extrema in EMD. Each derived
intrinsic mode is dominated by scales in a narrow range. Thus, according to the scale, the concrete
implications of each mode can be identified. For example, an intrinsic mode derived from an
economic time series with a scale of threemonths can often be recognized as the seasonal component.
Since data is the only linkwe have with the reality, by exploring data's intrinsicmodes, EMDnot only
helps discover the characteristics of the data but also helps understand the underlying rules of reality.
EMDwas initially proposed for study of ocean waves, and then successfully applied inmany areas,
such as biomedical engineering, structured health monitoring, earthquake engineering, and global
primary productivity evolution. However, these applications are mainly limited to studies of nature
science and engineering. There have been only two successful applications in social sciences so far.
The first is to apply EMD to financial data, which is used to examine the changeability of the markets