Estimating Keynesian models of business fluctuations using Bayesian Maximum Likelihood

Christian Schoder

No 162-2016, IMK Working Paper from IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute

Abstract: 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.

Keywords: Post-Keynesian economics; Bayesian Maximum Likelihood; Bayesian Vector Auto-Regression; model estimation; model evaluation (search for similar items in EconPapers)
JEL-codes: E12 E32 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2016
New Economics Papers: this item is included in nep-mac and nep-pke
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Journal Article: Estimating Keynesian models of business fluctuations using Bayesian Maximum Likelihood (2017)
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