时间：2015/10/23，9：00-11：00

地点：yh86银河国际一楼院史馆

主讲人：Heiner Mikosch

主讲题目： Forecasting Chinese GDP Growth with Mixed Frequency Data: Which Indicators to Look at?

主讲人介绍：Heiner Mikosch is a Senior Researcher at KOF. He completed the Gerzensee Ph.D. Program in Economics and received a Ph.D. from the ETH Zurich in 2011. Since 2011 he is a member of the Euro Area Business Cycle Network (EABCN).

内容简介：Building on a mixed data sampling (MIDAS) model we evaluate the predictive power of a broad set of 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 study how forecast errors evolve as ever new data get released over time. This procedure allows us to detect how useful specific indicators are for forecasting Chinese GDP growth at a specific horizon compared to alternative 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 inflation 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 traffic nor the manufacturing purchasing managers' index of the Chinese National Bureau of Statistics help much for now- or forecasting. Our results are of interest 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