Quantile-based Nonlinear Impact of Artificial Intelligence and Economic Policy Uncertainty on Education and Training Market in China

Abstract

In recent years, with the rapid development of artificial intelligence (AI) technology and the intensification of global economic policy uncertainty (EPU), China's education and training market (ETM) is facing unprecedented challenges and opportunities. This paper analyzed the quantile-based nonlinear impact of AI and EPU on ETM in China, and the results are as follows: 1. The nonparametric quantile causality test shows that there is a unidirectional causal relationship between AI and EPU, AI and ETM, as well as EPU and ETM; 2. The cross-quantilogram indicates that there is a quantile dependence among the three: the positive predictive effect of AI on ETM is mainly concentrated in bullish markets, the negative predictive effect of EPU on ETM is mainly concentrated in periods of policy stability, and there is an interaction between AI and EPU (AI promotes EPU in bullish markets, while EPU promotes AI during periods of economic stability); 3. The GARCH-Conditional quantile regression- model reveals the asymmetry of risk spillovers—the intensity of upside risk spillovers is far greater than that of downside ones. The risk spillover from AI to ETM is characterized by high volatility and strong extremeness, while the impact of EPU is relatively moderate but more persistent. The results suggested that policy makers, education and training organizations should comprehensively consider AI and EPU to cope with market uncertainty and ensure the stability and sustainability of ETM in China.