Non-parametric Determination of Real-Time Lag Structure between Two Time Series: the “Optimal Thermal Causal Path” Method

We introduce a novel non-parametric methodology to test for the dynamical time evolution of the lag-lead structure between two arbitrary time series. The method consists in constructing a distance matrix based on the matching of all sample data pairs between the two time series. Then, the lag-lead structure is searched as the optimal path in the distance matrix landscape that minimizes the total mismatch between the two time series, and that obeys a one-to-one causal matching condition. To make the solution robust to the presence of large noise that may lead to spurious structures in the distance matrix landscape, we then generalize this optimal search by introducing a fuzzy search by sampling over all possible paths, each path being weighted according to a multinomial logit or equivalently Boltzmann factor proportional to the exponential of the global mismatch of this path. We present the efficient transfer matrix method that solves the problem and test it on simple synthetic examples to demonstrate its properties and usefulness compared with the standard running-time cross-correlation method. We then apply our ‘Optimal Thermal Causal Path” method to the question of the causality between the US stock market and the treasury bond yields and confirm our earlier results on a causal arrow of the stock markets preceding the Federal Reserve Funds adjustments as well as the yield rates at short maturities in the period 2000-2003. Our application of this technique to inflation, inflation change, GDP growth rate and unemployment rate unearths non-trivial “causal” relationships: the GDP changes lead inflation especially since the 1980s, inflation changes leads GDP only in the 1980 decade, and inflation leads unemployment rates since the 1970s. In addition, our approach seems to detect multiple competing causality paths in which one can have inflation Preprint submitted to Elsevier Science 2 February 2008 leading GDP with a certain lag time and GDP feeding back/leading inflation with another lag time.