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EN
The goal of this paper is to determine whether there exist asymmetric shocks and structural differences between the Czech economy and the Euro Area 12. A New Keynesian DSGE model of a small open economy is used for this purpose. Asymmetric shocks and structural differences are examined in two ways. At first, I examine asymmetry of shocks and sources of structural differences, using model comparison based on the Bayes factor. I do not find substantial evidence in favor of heterogeneity in household preferences. I find slight differences in price and wage formation and substantial difference in interest rate smoothing. However, the main differences are in timing, persistence and volatility of structural shocks. I also investigate impact of structural differences and differences in persistence and volatility of structural shocks on the behavior of both economies, using analysis of impulse-response functions. I find no substantial differences in responses of the main variables to preference shocks. On the other hand, I find much larger volatility and persistence of domestic technology shocks. This contributes to the fact that responses of domestic variables to technology shocks are much larger, and display more gradual and hump-shaped pattern than responses of foreign variables. I also find that responses of foreign variables to labour supply shocks are much more gradual and sluggish than responses of domestic variables. As regards monetary shocks, I find that there is almost no response of foreign inflation to foreign monetary shock while response of domestic inflation to domestic monetary shock displays substantial decline followed by gradual recovery. Responses of foreign variables to cost-push shocks are larger and more volatile than responses of domestic variables.
EN
In this paper I present the historical, theoretical and empirical background of DSGE models. I show that the fundament of these models lies in optimizing agents framework and argue which impulses fueled the development of DSGE models. I demonstrate the evolution of DSGE models with an accent on the role and effects of the monetary policy, using distinction between RBC models and New Keynesian models. I explain the paradigm shift from the RBC models to the New Keynesian models by pointing out the main pitfalls of the RBC models and showing how adding nominal rigidities to the otherwise standard RBC models enhances empirical properties of these models. I also discuss how nominal rigidities are modeled in New Keynesian DSGE models and what the pros and cons of different approaches are. Finally, I review the most important New Keynesian theories of nominal rigidities and some of the empirical evidence on price and wage rigidities
EN
In this paper I propose a flexible trend specification for estimating DSGE models on log differences. I demonstrate this flexible trend specification on a New Keynesian DSGE model of two economies, which I consequently estimate on data from the Czech economy and the euro area, using Bayesian techniques. The advantage of the trend specification proposed is that the trend component and the cyclical component are modelled jointly in a single model. The proposed trend specification is flexible in the sense that smoothness of the trend can be easily modified by different calibration of some of the trend parameters. The results suggest that this method is capable of finding a very reasonable trend in the data. Moreover, comparison of forecast performance reveals that the proposed specification offers more reliable forecasts than the original variant of the model.
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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