Habit Formation, Price Indexation and Wage Indexation in the DSGE Model: Specification, Estimation and Model Fit

In order to determine which specification provides better fit of the data, this paper presents several specifications of a closed economy DSGE model with nominal rigidities. The goal of this paper is to find out whether some characteristics widely used in New Keynesian DSGE models, such as habit formation in consumption, price indexation and wage indexation, provide better fit of the macroeconomic data. Model specifications are estimated on the data of the US economy and Euro Area 12 economy, using Bayesian techniques, particularly the Metropolis-Hastings algorithm (using Dynare toolbox for Matlab). The data fit measure is a Bayes factor calculated from marginal likelihoods, acquired from Bayesian estimation. Results suggest that including habit formation in consumption significantly improves the empirical data fit of the model, whereas including partial price indexation and partial wage indexation does not improve the empirical data fit of the model. Variants with full price indexation and full wage indexation were the worst ones concerning their data fit.