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Forecasting Chinese GDP Growth with Mixed Frequency Data

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Abstract

Building on a mixed data sampling (MIDAS) model we evaluate the predictive power of a variety of monthly macroeconomic indicators for forecasting quarterly Chinese GDP growth. We iterate the evaluation over forecast horizons from 370 days to 1 day prior to GDP release and track the release days of the indicators so as to only use information which is actually available at the respective day of forecast. This procedure allows us to detect how useful a specic indicator is at a specic forecast horizon relative to other indicators. Despite being published with an (additional) lag of one month the OECD leading indicator outperforms the leading indicators published by the Conference Board and by Goldman Sachs. Albeit being smaller in terms of market volume, the Shenzhen Composite Stock Exchange Index outperforms the Shanghai Composite Stock Exchange Index and several Hong Kong Stock Exchange indices. Consumer price in ation is especially valuable at forecast horizons of 11 to 7 months. The reserve requirement ratio for small banks proves to be a robust predictor at forecast horizons of 9 to 5 months, whereas the big banks reserve requirement ratio and the prime lending rate have lost their leading properties since 2009. Industrial production can be quite valuable for now- or even forecasting, but only if it is released shortly after the end of a month. Neither monthly retail sales, investment, trade, electricity usage, freight trac nor the manufacturing purchasing managers' index of the Chinese National Bureau of Statistics help much for now- or forecasting. Our results might be relevant for experts who need to know which indicator releases are really valuable for predicting quarterly Chinese GDP growth, and which indicator releases have less predictive content.

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  • Heiner Mikosch & Ying Zhang, 2014. "Forecasting Chinese GDP Growth with Mixed Frequency Data," KOF Working papers 14-359, KOF Swiss Economic Institute, ETH Zurich.
  • Handle: RePEc:kof:wpskof:14-359
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    File URL: http://dx.doi.org/10.3929/ethz-a-010184765
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    1. Mehrotra, Aaron & Pääkkönen, Jenni, 2011. "Comparing China's GDP statistics with coincident indicators," Journal of Comparative Economics, Elsevier, vol. 39(3), pages 406-411, September.
    2. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    3. Marie Diron, 2008. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 371-390.
    4. Diron, Marie, 2006. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Working Paper Series 622, European Central Bank.
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    1. repec:eee:ecmode:v:66:y:2017:i:c:p:201-213 is not listed on IDEAS
    2. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland, Institute for Economies in Transition.

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    Keywords

    Forecasting; Mixed frequency data; MIDAS; China; GDP growth;

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