Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States

This paper demonstrates a mechanism whereby rules can be extracted from a feedforward neural network trained to characterize the inflation "pass-through" problem in American monetary policy, defined as the relationship between changes in the growth rate(s) of individual commodities and the economy-wide rate of growth of consumer prices. Monthly price data are encoded and used to train a group of candidate connectionist architectures. One candidate is selected for rule extraction, using a custom decompositional extraction algorithm that generates rules in human-readable and machine-executable form. Rule and network accuracy are compared, and comments are made on the relationships expressed within the discovered rules. The types of discovered relationships could be used to guide monetary policy decisions.