The Monetary Transmission Mechanism in the Czech Republic (evidence from VAR analysis)

Due to significant lags between a monetary policy action and the subsequent responses in the economy, understanding the transmission mechanism is of primary importance for conducting monetary policy. This paper analyses the monetary policy transmission mechanism using VAR models - the most widely used empirical methodology for analyzing the transmission mechanism in the Czech economy. Using the VAR methodology, the paper tries to evaluate the effects of an exogenous shock to monetary policy. The results show that an unexpected monetary policy tightening leads to a fall in output, whereas prices remain persistent for a certain time. The exchange rate reaction then heavily depends on the data sample used. Although it is clear that due to the rather short time span of the data, the results should be taken with caution, they at least show that the basic framework of how monetary policy affects the economy does not differ significantly either from what would be predicted by the theory or from the results obtained for more developed economies.Impulse response, monetary policy, transmission mechanism, vector autoregressions.

Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators

We evaluate the out-of-sample forecasting performance of six competing models at horizons of up to three quarters ahead in a pseudo-real time setup. All the models use information in monthly indicators released ahead of quarterly GDP. We estimate two models – averaged vector autoregressions and bridge equations – relying on just a few monthly indicators. The remaining four models condition the forecast on a large set of monthly series. These models comprise two standard principal components models, a dynamic factor model based on the Kalman smoother and a generalized dynamic factor model. We benchmark our results to the performance of a naïve model and the historical near-term forecasts of the Czech National Bank’s staff. The findings are also compared with a related study conducted by ECB staff (Barhoumi et al., 2008). In the Czech case, standard principal components is the most precise model overall up to three quarters ahead. However, the CNB staff’s historical forecasts were the most accurate one quarter ahead.Bridge models, dynamic factor models, GDP forecasting, principal components, real-time evaluation.

Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators

The authors evaluate the out-of-sample forecasting performance of six competing models at horizons of up to three quarters ahead in a pseudo-real time setup. All the models use information in monthly indicators released ahead of quarterly GDP. The authors estimate two models – averaged vector autoregressions and bridge equations – relying on just a few monthly indicators. The remaining four models condition the forecast on a large set of monthly series. These models comprise two standard principal components models, a dynamic factor model based on the Kalman smoother, and a generalized dynamic factor model. The authors benchmark their results to the performance of a naive model and the historical near-term forecasts of the Czech National Bank’s staff. The findings are also compared with a related study conducted by ECB staff (Barhoumi et al., 2008). In the Czech case, standard principal components is the most precise model overall up to three quarters ahead. However, the CNB staff’s historical forecasts were the most accurate one quarter ahead.GDP forecasting, bridge models, principal components, dynamic factor models, real-time evaluation