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Adaptive Learning and Labor Market Dynamics
Author(s) -
DI PACE F.,
MITRA K.,
ZHANG S.
Publication year - 2021
Publication title -
journal of money, credit and banking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.763
H-Index - 108
eISSN - 1538-4616
pISSN - 0022-2879
DOI - 10.1111/jmcb.12764
Subject(s) - autoregressive model , economics , unemployment , wage , adaptive expectations , adaptive learning , matching (statistics) , shock (circulatory) , feature (linguistics) , incentive , productivity , econometrics , value (mathematics) , simple (philosophy) , efficiency wage , term (time) , rational expectations , microeconomics , labour economics , computer science , macroeconomics , mathematics , artificial intelligence , statistics , machine learning , philosophy , linguistics , epistemology , medicine , quantum mechanics , physics
The standard search and matching model with rational expectations is well known to be unable to generate amplification in unemployment and vacancies. We document a new feature that cannot be replicated: properties of wage forecasts published by institutions in the near term. A parsimonious model with adaptive learning can provide a solution to both of these problems. Firms choose vacancies by forecasting wages using simple autoregressive models; they have greater incentive to post vacancies at the time of a positive productivity shock because of overoptimism about the discounted value of expected profits.