We use a Bayesian Markov Chain Monte Carlo algorithm to estimate the parameters of a "true" data-generating mechanism and those of a sequence of approximating models that a monetary authority uses to guide its decisions. Gaps between a true expectational Phillips curve and the monetary authority's approximating nonexpectational Phillips curve models unleash inflation that a monetary authority that knows the true model would avoid. A sequence of dynamic programming problems implies that the monetary authority's inflation target evolves as its estimated Phillips curve moves. Our estimates attribute the rise and fall of post-WWII inflation in the United States to an intricate interaction between the monetary authority's beliefs and economic shocks. Shocks in the 1970s made the monetary authority perceive a tradeoff between inflation and unemployment which ignited big inflation. The monetary authority's beliefs about the Phillips curve changed in ways that account for former Federal Reserve Chairman Paul Volcker's conquest of U.S.
ASJC Scopus subject areas
- Economics and Econometrics