Due to the vast amount of economic and financial information to be stored and analyzed, the need for the study of high dimensional networks has increased dramatically. Typical approaches for determining the groupwise information to infer statistical networks include lasso and large covariance matrix estimation regularizations. We investigate the application of the nodewise lasso algorithm to U.S. economic and financial data over the past 50 years. We use the nodewise lasso to estimate statistical networks of varying sparsity levels to describe the conditional dependence structure of a dataset consisting of 131 U.S. macroeconomic time series. With these estimated networks, we describe how they can be chosen from and interpreted in the context of both the statistical literature and the existing economic theory to enlarge our knowledge of economic and financial network structure. In particular, we focus on four key categories of economic indicators: housing starts, employment, consumer price index, and interest rates. For example, in a statistical network determined by the nodewise lasso, edges are found between nodes representing housing starts and nodes representing employment, industrial production, and interest rates. The housing starts nodewise lasso results, along with many others, are consistent with existing conclusions found in the study of economics and finance.