Consumer Confidence Indexes in New EU Member States

One of the most important economic indicators developed on the basis of agents’ opinions is consumer confidence index. Such a situation stems from the fact that consumption is usually the most important element of total demand. In well developed economies in which consumer confidence indexes have been used for many years a lot of attention is paid to analysis of their behavior. They are the element of composite leading indicators developed among others by the European Commission and the US Trade Department. In the emerging economies, in which analyses of consumer behavior were introduced relatively recently, qualitative data on consumer confidence is treated with less attention. This suggests conducting research in such a field for new EU member states. Countries which joined EU in 2004 r. are: the Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia, Slovakia. This group of countries was subject to analysis presented in the paper. The main aim of the research is to find and analyze relationships between consumer confidence indexes and chosen macroeconomic variables. Research questions refer to common direction in such an analysis conducted worldwide: to identify factors influencing consumers’ opinion and check whether changes of consumer confidence lead to changes of chosen economic variables. Consumption in the state of equilibrium depends on propensity to consumption (measured by confidence index) and on possibilities which are represented by disposable income. Quantitative monthly data used in the research refers to purchasing power of households and consumption expenditure. On the other hand the scope of the research was aimed at main economic time series which can influence agents expectations. Composite consumer confidence index cannot be commonly applied to describe volatility of various types of consumption, so in the research simple (component) indicators were used as well. The analysis of linear relationships is based on the cross correlations. In order to find lags and leads there were estimated Pearson correlation coefficients for shifts ±12 months. The analysis of linear relationships was extended by Granger’s causality test in order to verify whether quantitative variables influence consumer’s answers. Reverse relationships were also verified. In order to track nonlinear relationships neural networks module of Statistica was used. In the case algorithm of optimal data set for model time series was applied. Achieved results allow to identify relationships between analyzed economic time series, but also can be treated as first step for introducing consumer confidence indicators to economic forecasting in chosen developing EU economies..