Estimating Keynesian models of business fluctuations using Bayesian Maximum Likelihood

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractAn 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 impulseresponses 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 theoryimplied 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 162February 2016 Estimating Keynesian models of business fluctuations using Bayesian Maximum Likelihood. * Christian Schoder † January 23, 2016 AbstractAn 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 transmis...

Omitted-variable bias in demand-regime estimations: the role of household credit and wage inequality in Brazil

“…The VAR method, on the other hand, estimates simultaneously the relationship between demand and distribution, thus dropping the exogeneity assumption and the need to add up point coefficients to determine the demand regime. However, results seem to be very sensitive to the number of lags used for the dependent variables and to the ordering and type of impulse-response functions (Schoder 2017). Onaran and Galanis (2012) also argues that the interpretation of VAR estimations is sinuous, for it does not allow us to distinguish between the open-and closed-economy effects.…”

“…Many factors highlighted by the literature may help to explain the variety and fragility of results: omitted variables (Stockhammer 2017), different time horizons in the response of demand components (Blecker 2015), endogeneity of income distribution (Skott 2015), structural breaks and possible non-linearities (Mendieta-Munoz et al 2020;Nikiforos and Foley 2012;Palley 2014), and the need to consider other markets in addition to the goods markets in the analysis (Mendieta-Munoz et al 2020;Schoder 2017). In this context, this study aims to analyse the relationship between income distribution and aggregate demand in Brazil from 1995 to 2014.…”