A Constant Gain Learning Explanation of U.S. Post War Inflation and Unemployment

An adaptive learning model, similar to Primiceri (Q J Econ 121(3):867–901, 2007) is built, where policymakers use constant gain learning algorithm to update their knowledge of the model every time period. This framework is used to study post war US inflation and unemployment. The model accurately explains the Great Inflation and gives us interesting results—while the rational expectations equilibrium is characterized by low inflation, learning leads to disequilibrium dynamics when initial knowledge of the model is incorrect. Specifically, policymakers in 1960s under estimated the natural rate, persistence of inflation and slope of Phillips Curve. Hence, policy was more expansionary than optimal, resulting in inflation. The convergence of beliefs to rational expectations equilibrium explains the subsequent disinflation in 1980s. This study differs from Primiceri (2007) in the following ways: (i) The results establish the presence of time inconsistency in policy between 1963–1979. Time inconsistency and the resulting inflation bias is explained as the rational, endogenous outcome of evolving beliefs. Hence, the possibility of a repetition of 70s cannot be eliminated in the future, for similar outcomes can be expected under similar beliefs. (ii) We find evidence against the narrative of ‘Volcker disinflation’, which credits the disinflation of 80s to the appointment of Volcker, an ‘inflation hawk’, as the chairman of Federal Reserve. (iii) Further, as an extension of Primiceri (2007), who provides point estimates, time varying estimates of persistence of inflation and slope of Phillips Curve are estimated to provide quantitative precision to the narrative proposed. This extension essentially arises from the Lucas critique. (iv) Time varying sacrifice ratio is calculated for the entire sample period, which, to our knowledge, is the first attempt for the US economy.