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CNB > Economic research > Research publications > CNB Working paper series > 2010 > Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators

Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators

Kateřina Arnoštová, David Havrlant, Luboš Růžička, Peter Tóth

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.

JEL codes: C22, C32, C38, C52, C53, E23, E27

Keywords: Bridge models, dynamic factor models, GDP forecasting, principal components, real-time evaluation

Issued: December 2010

Download CNB WP No 12/2010 (pdf, 329 kB)

Published as: Arnoštová, K., Havrlant, D., Růžička, L., Tóth, P. (2011): Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators. Czech Journal of Economics and Finance, 61(6), 566–583.