摘要：We present a quantitative characterisation of the fluctuations of the annualized growth rate of the real US GDP per capita at many scales, using a wavelet transform analysis of two data sets, quarterly data from 1947 to 2015 and annual data from 1800 to 2010. The chosen mother wavelet (first derivative of the Gaussian function) applied to the logarithm of the real US GDP per capita provides a robust estimation of the instantaneous growth rate at different scales. Our main finding is that business cycles appear at all scales and the distribution of GDP growth rates can be well approximated by a bimodal function associated to a series of switches between regimes of strong growth rate ρhigh\rho_\text{high} and regimes of low growth rate ρlow\rho_\text{low}. The succession of such two regimes compounds to produce a remarkably stable long term average real annualized growth rate of 1.6% from 1800 to 2010 and ≈2.0%\approx 2.0\% since 1950, which is the result of a subtle compensation between the high and low growth regimes that alternate continuously. Thus, the overall growth dynamics of the US economy is punctuated, with phases of strong growth that are intrinsically unsustainable, followed by corrections or consolidation until the next boom starts. We interpret these findings within the theory of "social bubbles" and argue as a consequence that estimations of the cost of the 2008 crisis may be misleading. We also interpret the absence of strong recovery since 2008 as a protracted low growth regime ρlow\rho_\text{low} associated with the exceptional nature of the preceding large growth regime.
关键词：wavelet transform;economic growth;GDP per capita;business cycle;multi-scale analysis PACS: 89.65.Gh;05.45.Tp
