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

Venkata Raamasrinivas Mangapuram ()
Additional contact information
Venkata Raamasrinivas Mangapuram: Madras School of Economics

Journal of Quantitative Economics, 2022, vol. 20, issue 3, No 9, 721 pages

Abstract: Abstract 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.

Keywords: Constant gain (CG) learning; Natural rate of unemployment; Persistence; Kalman filter; Maximum likelihood estimation; Time inconsistency; Sacrifice ratio (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40953-022-00315-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jqecon:v:20:y:2022:i:3:d:10.1007_s40953-022-00315-w

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40953

DOI: 10.1007/s40953-022-00315-w

Access Statistics for this article

Journal of Quantitative Economics is currently edited by Dilip Nachane and P.G. Babu

More articles in Journal of Quantitative Economics from Springer, The Indian Econometric Society (TIES) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().