  We propose a method of analyzing multivariate time series data that
investigates lead-lag relationships among economic indicators during the
COVID-19 era with a weighted directed network of lagged variables. The analysis
includes a stock index, average unemployment, and several variables that are
used to calculate inflation. Three complex networks are created, with these
variables and several lags of each as the network nodes. Network edges are
weighted based on three relationship metrics: correlation, mutual information,
and transfer entropy. In each network, nodes are merged, and edges are
aggregated to simplify the weighted directed graph. Pagerank is used to
determine the most influential and the most influenced node over the time
period. Results were reasonably robust within each network, but they were
heavily dependent on the choice of metric.
