Mixed Frequency Forecasts for Chinese GDP
Philipp Maier ()
Staff Working Papers from Bank of Canada
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
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
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:11-11
Access Statistics for this paper
More papers in Staff Working Papers from Bank of Canada 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada. Contact information at EDIRC.
Bibliographic data for series maintained by ().