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EN
Background: Although there are many formal models about interactions among habit formation, preference change and the economic growth, only a few formal models examine implications of habit formation and preference change for the economic growth with resources. Objectives: This paper builds an economic growth model with endogenous physical capital, renewable resources, habit formation and preference. Methods: Although it is influenced by the Ramsey growth theory with time preference and habit formation, the paper applies a new approach to the household behaviour. Results: We plot the motion of the economy and conduct a comparative dynamic analysis with regard to certain parameters to obtain insights into interactions between the preference and the economic structural changes. Conclusions: We have shown that habit formation and preference change have significant effects on the economic grow and resources utilization both with regard to the transitional paths and the long-run equilibrium.
EN
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.
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