 Short-Term Forecasting of Czech Quarterly GDP Using Monthly IndicatorsKateřina Arnoštová, David Havrlant, Luboš Růžička and Peter Tóth Working Papers from Czech National Bank, Research and Statistics Department Abstract: 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. Keywords: Bridge models; dynamic factor models; GDP forecasting; principal components; real-time evaluation. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:cnb:wpaper:2010/12 Access Statistics for this paper More papers in Working Papers from Czech National Bank, Research and Statistics Department Contact information at EDIRC. |
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