This thesis assesses the usefulness of confidence indicators for short term forecasting of the economic activity in the Czech Republic. The predictive power of both the business confidence indicator and the customer confidence indicator is examined using two empirical approaches. First we predict the likelihood of economic downturn defined as a discrete event using logit models, later we estimate GDP growth out of sample forecasts in the framework of vector autoregression models. The results obtained from the downturn probability models confirm the ability of confidence indicators (especially the business confidence indicator) to estimate the current economic situation and to anticipate economic downturn one quarter ahead. Results from the out-of-sample GDP growth value forecasting are ambiguous. Nevertheless the customer confidence indicator significantly improved original forecasts based on a model with standard macroeconomic variables and therefore we conclude in favour of its predictive power. This result was indirectly confirmed by OECD as the Czech customer confidence indicator has been included as a new component in the OECD domestic composite leading indicator since April 2012.

The thesis is concerned with Multi-Criteria Decision Making, in particular the Analytic Network Process method. The introductory part is dedicated to compile all the theory necessary to understand the method and utilized throughout the paper. The Analytic Hierarchy Process method is described and later generalized in the form of the ANP. Part of the paper is a description of available software products that are able to solve the ANP models. The main focus is on the application of the method, the usability and efficiency of possible use in macroeconomics is tested. In the third chapter a macroeconomic forecasting model is developed in cooperation with experts. The results of this model are compared with the best available up-to-date forecasts by local and international institutions.

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