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EN
The central goal of this paper is to analyse factors that determine the growth of shadow banking in 11 new EU member states from Central and Eastern Europe, using annual data for the period 1999 – 2019. As the levels of economic and financial development vary considerably across these countries, we split them into three more homogenous groups: the Balkan, Baltic, and Višegrad countries. We then applied dynamic and fully modified ordinary least squares to estimate the relationship between the variables. The results of our study indicate that the insurance and banking sectors as well as economic growth have a positive effect on the shadow banking sector across all groups. We also found that the global financial crisis (GFC) of 2007 – 2008 had a diverse impact on the selected groups of countries.
EN
Restrictions allows us to include economic theory into non-structural VECM models. They are useful in making coherency between two opposite school of econometrics: traditional structural simultaneous equation approach and the vector autoregression (VAR) models based on reduced form. The paper regards economic interpretation in the case of restricting particular matrices of VECM representation. Restrictions on short-term dependency matrix are connected with the rank of lags in the VECM model choice and the short-run exogeneity. The reduced rank condition with respect to the long-run multiplier matrix imply a lack of the system joint stationarity. Restrictions concern not only the vector error correction model parameter, but the matrices of its solution may be restricted too. In the case of the model with jointly stationary variables I(0) the vector moving average (VMA) representation is such solution, in the case of I(1) variables model - common I(1) trends model. The row restrictions imposed on orthogonal complements of cointegration matrix may be useful in the stationarity analysis of system variables and hence may be compared with the standard unit root tests results. On the other hand, restriction on adjustment matrix orthogonal complements are helpful in impulse - response analysis. Almost all economically interpreted restrictions may be included in the alternative (dual) form - the row restrictions on the original matrix may be performed as the column conditions on the orthogonal complement of such matrix. The likelihood ratio test allows us to verify such overidentifying restrictions. The table in the and of the paper clarifies the type of restrictions classification. The restrictions interpretation is rather simple in the case of I(1) model. The l(2) variables inclusion imply serious complications.
EN
This study offers a new perspective on the debate concerning inflation convergence in the Euro Area. A new pair-wise unit root testing procedure advocated by Pesaran (2007) is employed on all possible bivariate consumer price index differentials. Evidence in favour of long-run convergence is confirmed where the fraction of rejections in favour of stationary exceeds the size of the individual tests. We find evidence of long-run inflation convergence across the EU, though the speed of convergence is lower for current non-Euro Area countries.
EN
This paper deals with the analysis of the purchasing power parity between Latvia and the euro area and between Slovakia and the euro area using the Engle-Granger and Johansen co-integration techniques. Latvia and Slovakia became members of the European Union in May 2004 and have been already the members of the Exchange Rate Mechanism II (ERM II) preparing for the euro adoption. The whole analysis was done on monthly data covering the period January 1999 - May 2008. Both the Engle-Granger and the Johansen method did not confirmed the purchasing power parity (PPP) validity in both analysed cases.
EN
In the EU, USA and elsewhere in the world a significant amount of public money is being devoted to support of biofuel production. It is believed that rising biofuel production affects agricultural commodity prices as well as fossil fuel prices. The relationship between oil and food prices has been known for a long time. In this article the authors analyse the statistical relationship among the fuel prices (oil, gasoline, bioethanol) and selected food prices (maize, wheat and sugar). They conduct a series of statistical tests, starting with tests for unit roots, estimation of co-integrating relationships among the price series, evaluating the inter-relationship among the variables using Vector Error Correction Model (VECM) and Variance decomposition. According to their results, there is a long-run co-integrating relationship among the selected time series in the later years while the interrelationship among the variables was weaker in earlier period.
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