• Focus Area -
  • Type External journal article
  • Date 14 January 2011


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 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.

Authors: Arnoštová, K., Havrlant, D., Růžička, L., Tóth, P.


Czech Journal of Economics and Finance, vol. 61(6), pages 566-583


David Havrlant

David Havrlant

David was a research fellow who contributed a better understanding of the current and future economic environment of a changing… David was a research fellow who contributed a better understanding of the current and future economic environment of a changing region. He was mainly interested in the development of models for policy analysis and forecasting. At the same time, he was involved in projects related to the Vision 2030 program, focusing on the economic transformation and diversification of the Saudi economy. Prior to joining KAPSARC, David worked at the European Commission, European Central Bank, Moody's Analytics and the Czech National Bank. In these institutions, he participated in economic policy analysis, forecasting and research. He also served as a consultant to central banks in the CEE region, managing a variety of economic modeling projects. David led courses in econometrics and operations research during his Ph.D. studies.

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