Mixed Frequency Forecasts for Chinese GDP
AbstractWe 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.
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Bibliographic InfoPaper provided by Bank of Canada in its series Working Papers with number 11-11.
Length: 32 pages
Date of creation: 2011
Date of revision:
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Econometric and statistical methods; International topics;
Find related papers by JEL classification:
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-04-09 (All new papers)
- NEP-CBA-2011-04-09 (Central Banking)
- NEP-FOR-2011-04-09 (Forecasting)
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- Huang, Y-F., 2012. "Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach," MPRA Paper 41933, University Library of Munich, Germany.
- Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.
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