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Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators

Kateřina Arnoštová, David Havrlant, Luboš Rùžièka () and Peter Tóth ()
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Luboš Rùžièka: Czech National Bank,

Authors registered in the RePEc Author Service: Luboš Růžička

Czech Journal of Economics and Finance (Finance a uver), 2011, vol. 61, issue 6, 566-583

Abstract: 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.

Keywords: GDP forecasting; bridge models; principal components; dynamic factor models; real-time evaluation (search for similar items in EconPapers)
JEL-codes: C22 C32 C38 C52 C53 E23 E27 (search for similar items in EconPapers)
Date: 2011
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Handle: RePEc:fau:fauart:v:61:y:2011:i:6:p:566-583