Research on the Inflation Prediction Based on High-Frequency Stock Liquidity
Accurate prediction of inflation is crucial for investors and policymakers.The structural transformation of the economy presents unprecedented challenges to the forecasting of inflation rates.This study examined the impact of daily information on liquidity in the Chinese A-share market from June 1996 to December 2023 on the short-term prediction of inflation rates,based on the Autoregressive Mixed Data Sampling model(AR-MIDAS).The results indicated that the inclusion of daily liquidity indicators significantly enhances the predictive performance of the AR-MIDAS model for inflation rates.Innovatively,this study incorporated stock liquidity into the inflation forecasting model,expanding the literature on the use of financial market information to predict inflation.It provided a new perspective for understanding the connection between financial markets and the real economy,and also offers decision-making references for investors and policymakers.
NETL
NSTL
万方数据
