Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP

Konstantin Kholodilin () and Boriss Siliverstovs

No 10-251, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich

Abstract: This study utilizes the dynamic factor model of Giannone et al. (2008) in order to make now-/forecasts of GDP quarter-on-quarter growth rates in Switzerland. It also assesses the informational content of macroeconomic data releases for forecasting of the Swiss GDP. We find that the factor model offers a substantial improvement in forecast accuracy of GDP growth rates compared to a benchmark naive constant-growth model at all forecast horizons and at all data vintages. The largest forecast accuracy is achieved when GDP nowcasts for an actual quarter are made about three months ahead of the official data release. We also document that both business tendency surveys as well as stock market indices possess the largest informational content for GDP forecasting although their ranking depends on the underlying transformation of monthly indicators from which the common factors are extracted.

Keywords: Business tendency surveys; Forecasting; Nowcasting; Real-time data; Dynamic factor model (search for similar items in EconPapers)
Pages: 39 pages
Date: 2010-01
New Economics Papers: this item is included in nep-cba and nep-for
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:kof:wpskof:10-251

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