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<p>High-frequency macro-financial environment variables provide more useful information and are efficient in predicting the low-frequency GDP growth rate. To this end, we extend the traditional Growth-at-Risk (GaR) into a high-frequency GaR (HF-GaR). In this extension, we construct three high-frequency macro-financial environment indices using a mixed frequency dynamic factor model and then use a mixed data sampling-quantile regression method to measure China’s daily GaR from Jan 1, 2000, to Sep 30, 2024. The evidence shows that our HF-GaR has favorable prediction performance, with quantile mean absolute error and quantile root square error values less than 0.1 and is significantly superior to the traditional GaR at the 1% level for most quantiles. Additionally, HF-GaR can offer early warning of economic downturns, especially predicting China’s GDP growth rate at the 5% quantile less than 0 in 2020Q1. Moreover, we conduct a counterfactual scenario analysis and find that the conditional quantile of GDP growth rate changes as the macro-financial environment tightens or loosens. Finally, we also validated that the HF-GaR model is equally applicable in other economies.</p>

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High-frequency Growth-at-Risk of China: the Role of Macro-financial Environment

  • 期刊论文

  • 发表日期：

    2025年3月27日

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  • Mengnan Xu,
  • Qifa Xu,
  • Cuixia Jiang,
  • Xingxuan Zhuo

摘要

High-frequency macro-financial environment variables provide more useful information and are efficient in predicting the low-frequency GDP growth rate. To this end, we extend the traditional Growth-at-Risk (GaR) into a high-frequency GaR (HF-GaR). In this extension, we construct three high-frequency macro-financial environment indices using a mixed frequency dynamic factor model and then use a mixed data sampling-quantile regression method to measure China’s daily GaR from Jan 1, 2000, to Sep 30, 2024. The evidence shows that our HF-GaR has favorable prediction performance, with quantile mean absolute error and quantile root square error values less than 0.1 and is significantly superior to the traditional GaR at the 1% level for most quantiles. Additionally, HF-GaR can offer early warning of economic downturns, especially predicting China’s GDP growth rate at the 5% quantile less than 0 in 2020Q1. Moreover, we conduct a counterfactual scenario analysis and find that the conditional quantile of GDP growth rate changes as the macro-financial environment tightens or loosens. Finally, we also validated that the HF-GaR model is equally applicable in other economies.