Estimating Keynesian models of business fluctuations using Bayesian Maximum Likelihood

Arbeitspapier

An empirical approach to model estimation and evaluation based on Bayesian Maximum Likelihood is introduced to the post-Keynesian literature. To illustrate the method, it is applied to a neo-Kaleckian type of model of Euro Area business cycle fluctuations including endogenous fiscal and monetary policy as well as endogenous wage formation. To evaluate its empirical performance, the marginal likelihood and impulse-responses conditional on the proposed model are contrasted to those conditional on the corresponding Bayesian vector auto-regression models after relaxing the theory-implied cross-coefficient restrictions. The estimated parameter distributions are broadly in line with the empirical literature. Yet, a Bayesian vector auto-regression with loose theory-implied restrictions on the prior outperforms the neo-Kaleckian model considerably indicating misspecification. Further, a baseline Dynamic Stochastic General Equilibrium model is superior in terms of the marginal likelihood. Comparative impulse-response analysis indicates a failure of the neo-Kaleckian model to satisfyingly capture the fiscal and monetary policy transmission mechanisms.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IMK Working Paper ; No. 162

Classification and Topics

• Classification: Wirtschaft ; General Aggregative Models: Keynes; Keynesian; Post-Keynesian ; Business Fluctuations; Cycles
• Subject: Post-Keynesian economics ; Bayesian Maximum Likelihood ; Bayesian Vector Auto-Regression ; model estimation ; model evaluation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schoder, Christian
• Event: Veröffentlichung
• (who): Hans-Böckler-Stiftung, Institut für Makroökonomie und Konjunkturforschung (IMK)
• (where): Düsseldorf
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/144919
• URN: urn:nbn:de:101:1-201602195098
• Last update: 10.03.2025, 11:43 AM CET