C22 - Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion ProcessesReturn

We provide an extensive study assessing whether a prior wavelet-based denoising step enhances the forecast accuracy of standard forecasting models. Many combinations of attribute values of the thresholding (denoising) algorithm are explored together with several traditional forecasting models used in economic time series forecasting. The results are evaluated using M3 competition yearly time series. We conclude that the performance of a forecasting model combined with the prior denoising step is generally not recommended, which implies that a straightforward generalisation of some of the results available in the literature (which found the denoising step to be beneficial) is not possible. Even if cross-validation is used to select the value of the threshold, a superior performance of the forecasting model with the prior denoising step does not generally follow.

The objective of this paper is to present the results of an analysis of the indicator Greenhouse gas emissions (GGE). The GGE is one of the headline indicators tracked under the EU's main socioeconomic strategy until the year 2020 - the EUROPE 2020 strategy for smart, sustainable and inclusive growth. In the area of sustainable growth, the Resource-efficient Europe initiative was established. For 2020, the EU has made a unilateral commitment to reduce overall greenhouse gas emissions from its 28 member states by 20% compared to 1990 levels. The GGE indicator shows total man-made emissions of the so-called Kyoto basket of greenhouse gases. It presents annual total emissions in relation to those observed in 1990. The aggregate greenhouse gas emissions are expressed in units of CO2 equivalent.
The development of the GGE indicator in the EU and in the Czech Republic from 2000 to 2014 from the point of view of the ability to achieve the Strategy objectives is analyzed. In terms of the level of the analyzed indicator, the set of the EU countries can be divided into two significantly different groups - the euro area and the non-euro area; or into groups by the year of their joining the EU. Analyses of the dependency between the growth of GGE and the GDP growth for both the set of the 28 EU countries and the Czech Republic are presented as well.

The paper investigates developments of exchange rate time series of Central European currencies and tries to find evidence of some stylised facts. Statistical methods and an econometric approach to the univariate time series modelling of high-frequency data, i.e., daily, are used. The main conclusions are as follows: (1) All the CE nominal exchange time series are not stationary: nevertheless, stationarity of all the return time series was confirmed. (2) Volatility clustering was proven and the GARCH modelling approach was successfully applied, including asymmetric modelling of volatility. (3) The more flexible an exchange rate regime is, the more volatile the respective currency. This is true for both nominal and real exchange rates. While nominal volatility is lower than real volatility in a system of fixed or less flexible exchange rates, the opposite is true for flexible systems: exchange rate volatility is higher in nominal terms than in real terms.

In the study submitted, selected methods of financial time-series analysis are applied to daily returns of the most liquid stocks at Czech capital market. In most cases, symmetric GARCH(1,1) models are quite satisfactory. Further, ARFIMA models enabling to catch "long memory" of underlying processes are suitable for the modelling both absolute values of returns and their volatility. Alternative posibility is to employ bilinear models, which prove to be suitable namely for returns. Hurst exponents computed signalize some tendency to cycles creation in some cases (ČEZ, Unipetrol).

Modifying IS-MP-IA model by using EU economic characteristics allows for better interpretation of the results. Specifically, from the point of view of the IS and MP curves we obtain useful information about the influence of EU economy on the Czech economy (i.e. on its GDP). We may conclude that the GARCH methodology seems to be a suitable tool for estimation of modified IS-MP-IA model and for subsequent anticipation expected development of basic macroeconomic variables, relevant for the Czech economy after its accession to EU.