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Mixed Frequency Forecasts for Chinese GDP

Philipp Maier ()

Staff Working Papers from Bank of Canada

Abstract: We evaluate different approaches for using monthly indicators to predict Chinese GDP for the current and the next quarter (‘nowcasts’ and ‘forecasts’, respectively). We use three types of mixed-frequency models, one based on an economic activity indicator (Liu et al., 2007), one based on averaging over indicator models (Stock and Watson, 2004), and a static factor model (Stock and Watson, 2002). Evaluating all models’ out-of-sample projections, we find that all the approaches can yield considerable improvements over naïve AR benchmarks. We also analyze pooling across forecasting methodologies. We find that the most accurate nowcast is given by a combination of a factor model and an indicator model. The most accurate forecast is given by a factor model. Overall, we conclude that these models, or combinations of these models, can yield improvements in terms of RMSE’s of up to 60 per cent over simple AR benchmarks.

Keywords: Econometric and statistical methods; International topics (search for similar items in EconPapers)
JEL-codes: C50 C53 E37 E47 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2011
New Economics Papers: this item is included in nep-cba and nep-for
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7) Track citations by RSS feed

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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:11-11

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