Henderson's mixed model equations system is generally required in a Gibbs sampling application. In two previous studies, we proposed two indirect solving approaches that give dominance values in an animal model context with no need to process all this system. The first one does not require D-1 and the second is based on processing the additive animal model residuals. In the present work, we show that these two methods can be handled iteratively. Since the Bayesian approach is now a widely used tool in estimation of genetic parameters, the main part of this work is devoted to a Gibbs sampling application that can be accelerated by means of the aforementioned indirect solving methods. Three replicates of a population data set are simulated in the paper to compare the applications and estimates. This shows effectively that the estimates given by implementing a Gibbs sampler with each of the two suggested solving methods are obtained with less computational time and are comparable to those given by considering the integral system, particularly when priors are more weighted.
We investigate the short-term dynamics of the Polish economy by means of a small-scale DSGE model with stochastic menu costs. We compare macroeconomic evidence of price rigidity in a model with the state-dependent Phillips curve to a benchmark model with conventional time-dependent price stickiness. With a moderate 2.3% upper boundary on menu costs the estimated state-dependent pricing model for Poland indicates a median duration of prices about 14 months, whereas the same measure of price stickiness in the time-dependent pricing model is 3 months shorter. The result from the state-dependent pricing model estimated from macro data is closer to, both, micro-price evidence, and surveys on frequency of price changes in Poland. The difference is explained by a selection effect being present in the model with state-dependent price stickiness, only. It yields more intense and impact price adjustment after a monetary policy shock.
The paper deals with the relationship between provided credit and non-per-forming loans (NPL) in the Czech Republic (CR). In the period 1994 – 2016 the CR experienced both periods of rapid credit growth and the transition to market economy followed by a strong convergence process. The aim of the paper is to investigate the effects of credit growth on the NPL dynamics. The selected method is Bayesian estimation with instrumental variables. Results demonstrate positive relationship between the credit growth and the NPL dynamics; however, estimated posterior mean values are rather small and imply that the credit growth influenced the accumulation of credit risk and the origination of the NPL in a modest way. Moreover, the effects are stronger in the CR compared to the prior value (close to zero), which is calculated based on the information obtained from the international empirical studies.
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