Premium
Non‐stationary Hours in a DSGE Model
Author(s) -
CHANG YONGSUNG,
DOH TAEYOUNG,
SCHORFHEIDE FRANK
Publication year - 2007
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/j.1538-4616.2007.00070.x
Subject(s) - dynamic stochastic general equilibrium , odds , econometrics , economics , bayesian probability , computer science , macroeconomics , logistic regression , monetary policy , machine learning , artificial intelligence
The time series fit of dynamic stochastic general equilibrium (DSGE) models often suffers from restrictions on the long‐run dynamics that are at odds with the data. Using Bayesian methods we estimate a stochastic growth model in which hours worked are stationary and a modified version with permanent labor supply shocks. If firms can freely adjust labor inputs, the data support the latter specification. Once we introduce frictions in terms of labor adjustment costs, the overall time series fit improves and the model specification in which labor supply shocks and hours worked are stationary is preferred.