In the study we introduce an extension to a stochastic volatility in mean model (SV-M), allowing for discrete regime switches in the risk premium parameter. The logic behind the idea is that neglecting a possibly regimechanging nature of the relation between the current volatility (conditional standard deviation) and asset return within an ordinary SV-M specication may lead to spurious insignicance of the risk premium parameter (as being ‛averaged out’ over the regimes). Therefore, we allow the volatility-in-mean eect to switch over dierent regimes according to a discrete homogeneous two-state Markov chain. We treat the new specication within the Bayesian framework, which allows to fully account for the uncertainty of model parameters, latent conditional variances and hidden Markov chain state variables. Standard Markov Chain Monte Carlo methods, including the Gibbs sampler and the Metropolis-Hastings algorithm, are adapted to estimate the model and to obtain predictive densities of selected quantities. Presented methodology is applied to analyse series of the Warsaw Stock Exchange index (WIG) and its sectoral subindices. Although rare, once spotted the switching in-mean eect substantially enhances the model t to the data, as measured by the value of the marginal data density.

This paper studies the long-run relationship between consumption, labour income and asset wealth in Poland. Within cointegrated VAR model dynamic responses of the variables in the system to shocks are studied. In addition, series are decomposed into permanent and transitory components on the basis of the cointegrating relation found in the system.

Main conclusion of this paper is that deviations of the three variables from their estimated long-run relationship are better explained with uctuations of labour income than assets. A tentative explanation of this nding is presented. Additionally, the magnitude of the asset wealth eect in Poland is calculated and compared with other studies for European countries and for the U.S.

The economy of Slovakia experienced a turning point in the 1st half of 2008 and entered a phase of decline. The negative impacts of the global economic crisis became evident in the 2nd half of 2008 and led into a recession in the 1st quarter of 2009. The composite leading indicator was originally intended for forecasting of business cycle turning points between the decline and growth phases. The aim of this paper is to transform the qualitative information from composite leading indicator into quantitative forecast and verify whether the beginning of recession in Slovakia could have been identied in advance. The ARIMAX and error correction models are used for the composite reference series and GDP forecasts respectively. The nal result shows that the composite leading indicator is useful not only for identifying turning points, but also for the prediction of recession phase.

We investigate the problem of setting revenue sharing rules in a team production environment with a principal and two agents. We assume that the project output is binary and that the principal can observe the level of agents’ actual eort, but does not know the production function. Identifying conditions that ensure the eciency of the revenue sharing rule, we show that the rule of equal percentage markups can lead to ination of project costs. This result provides an explanation for project cost overruns other than untruthful cost reporting.