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RELATIONSHIP BETWEEN FISCAL DECENTRALIZATION AND ECONOMIC GROWTH IN EUROPEAN UNION COUNTRIES AND SLOVAKIA

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Maličká L., Šuliková V., Šoltés M.
Ekonomický časopis (Journal of Economics)
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2017
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vol. 65
|
issue 9
856 – 875
EN
The results in the area related to fiscal decentralization and economic growth are frequently inconsistent and somewhat ambiguous, although the fiscal federalism theory clearly promotes the fiscal decentralization gains in favour of efficiency and economic growth. The paper focuses on investigating the inverted U-shaped relationship between fiscal decentralization and economic growth using the GMM model (Generalized Method of Moments). After these results were obtained, real values of Slovakia are compared to GMM – EU-26 trend. The results of GMM estimation include a threshold value of fiscal decentralization, revealing the point at which a positive relation between fiscal decentralization and economic growth turns into negative. GMM estimation of the EU-26 countries sample confirms the inverted U-shaped relationship in case of revenue and tax decentralization. Expenditure decentralization seems to be insignificant. The case of Slovakia shows the conformity with the EU trend, what is evident in the case of tax decentralization and less in revenue decentralization.
2

SOME ASPECTS OF THE GENERALIZED METHOD OF MOMENTS APPLICATION FOR TIME SERIES MODELS AND PANEL DATA MODELS

100%
Danska-Borsiak B.
Przegląd Statystyczny
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2008
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vol. 55
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issue 3
47-60
EN
One of the most popular methods of estimation of econometric models is Ordinary Least Squares (OLS). The desired properties of the OLS estimators depend on whether the method's assumptions are true. If not, another method of estimation should be applied. An universal method, that can be applied to models, in which the normality of the error term distribution is not assumed, is the Generalized Method of Moments (GMM). One of the main advantages of GMM is that it can be used to perform inference about the parameters in nonlinear dynamic models. The main goal of this paper is to present the key elements of GMM, and one of the possibilities of using it for inference in dynamic panel models
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