Buchbesprechungen

Ohne Zusammenfassung     

Einzelbesprechungen

Ohne Zusammenfassung     

Product Innovation,Process Innovation,and Size

We test the hypothesis that large firms devote a higher proportion of their research and development (R & D) expenditure on process innovation thansmaller firms. According to the estimates, process- and product R & D expenditure rise less than in proportion to size. The size effect is somewhat stronger for process R & D but the difference to product R & D is in no way dramatic. This difference with regard to size elasticity of process- and product R & D is somewhat more pronounced when accounting for possible interrelationships between expenditure on process- and product R & D but remains statistically non-significant.     

Causal relations between nominal exchange rates and monetary fundamentals in Central and Eastern European countries

The purpose of this paper is to investigate the causal relations between nominal exchange rates and monetary fundamentals. The analysis is conducted using panel quarterly data from the period 2001–12 for Central and Eastern European countries (including Turkey) with relatively flexible exchange rate regimes. The paper reconnects the empirical literature on exchange rates in Central and Eastern European countries with the most recent findings on exchange rate determination in advanced economies. Kóyna's approach, which accounts for linkages between countries, is used in the study. The main findings indicate the existence of causal relations running from both nominal exchange rates to monetary fundamentals and in the opposite direction as well as the existence of a relatively strong link between exchange rates and differentials in the relative price of non‐tradables.     

Non-classical Algorithm for Time Series Prediction of the Range of Economic Phenomena With Regard to the Interaction of Financial Market Indicators

The aim of the article is to present non-clasical copyrighted algorithm for prediction of time series, presenting macroeconomic indicators and stock market indices. The algorithm is based on artificial neural networks and multi-resolution analysis （the algorithm is based on Daubechies wavelet）. However, the main feature of the algorithm, which gives a good quality of the forecasts, is all included in the series analysis division into, a few partial under-series and prediction dependence on a number of other economic series. The algorithm used for the prediction, is copyrighted algorithm, labeled M.H-D in this article. Application of the algorithm was performed on a series presenting WIG 20. The forecast of WIG 20 was conditional on trading the Dow Jones, DAX, Nikkei, Hang Seng, taking into account the sliding time window. As an example application of copyrighted model, the forecast of WIG 20 for a period of two years, one year, six month was appointed. An empirical example is described. It shows that the proposed model can predict index with the scale of two years, one year, a half year and other intervals. Precision of prediction is satisfactory. An average absolute percentage error of each forecast was： 0.0099%---for two-year forecasts WIG 20; 0.0552%--for the annual forecast WIG 20; and 0.1788%---for the six-month forecasts WIG 20.