We show how fan charts generated from Bayesian vector autoregression models can be useful for assessing (i) the effect of the zero-lower-bound constraint on forecasting uncertainty and (ii) the credibility of stress tests conducted to evaluate financial stability. To illustrate these issues, we...

This paper shows how fan charts generated from Bayesian vector autoregression (BVAR) models can be useful for assessing 1) the forecasting accuracy of central banks’ prediction models and 2) the credibility of stress tests carried out to evaluate financial stability. Using unique data...

We explore a potential source of human capital spatial disparities: the unequal access to tertiary education caused by the absence/presence of a local university. Because the entrance to a university is a sequential process in the Czech Republic we model both a student's decision to apply to a...

We investigate the evolution of the monetary policy transmission mechanism in the Czech Republic over the 1996-2010 period by employing a time-varying parameters Bayesian vector autoregression model with stochastic volatility. We evaluate whether the response of GDP and the price level to...

The paper analyzes the macroeconomic effects of fiscal policy shocks in the Czech Republic. The low number of observations available for fiscal variables significantly affects the setup of the analysis. Firstly, a small-scale VAR is considered. Secondly, the model is estimated using Bayesian...

This paper concentrates on describing the available empirical findings on monetary policy transmission in the Czech Republic. Besides the overall impact of monetary policy on inflation and output, it is useful to study its individual channels, in particular the interest rate channel, the...

This paper examines the effect of non-linearities on density forecasting. It focuses on the relationship between credit markets and the rest of the economy. The possible non-linearity of this relationship is captured by a threshold vector autoregressive model estimated on the US data using...