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Nowcasting real GDP growth with business tendency surveys data: A cross country analysis

Evžen Kočenda () and Karen Poghosyan

No 1002, KIER Working Papers from Kyoto University, Institute of Economic Research

Abstract: We use nowcasting methodology to forecast the dynamics of the real GDP growth in real time based on the business tendency surveys data. Nowcasting is important because key macroeconomic variables on the current state of the economy are available only with a certain lag. This is particularly true for those variables that are collected on a quarterly basis. To conduct out‐of‐sample forecast evaluation we use business tendency surveys data for 22 European countries. Based on the different dataset and using outof‐sample recursive regression scheme we conclude that nowcasting model outperforms several alternative short‐term forecasting statistical models, even when the volatility of the real GDP growth is increasing both in time and across different countries. Based on the Diebold‐Mariano test statistics, we conclude that nowcasting strongly outperforms BVAR and BFAVAR models, but comparison with AR, FAAR and FAVAR does not produce sufficient evidence to prefer one over another.

Keywords: Nowcasting; short‐term forecasting; dynamic and static principal components; Bayesian VAR; Factor Augmented VAR; real GDP growth; European OECD countries (search for similar items in EconPapers)
JEL-codes: E52 C33 C38 C52 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eec, nep-ets, nep-for and nep-mac
Date: 2018-09
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