Averaged static connectedness.

Most of the existing studies on the connectedness among economic policy uncertainties (EPUs) usually neglect the quantile and frequency domain perspectives. To address this limitation, this paper proposes a quantile time–frequency connectedness model to analyze the connectedness among EPUs by combining the quantile and frequency domain dimensions. First, the quantile-vector autoregressive model (QVAR(p)) is estimated and converted into the quantile-vector moving average representation (QVMA()). Next, the generalized prediction error variance decomposition (GFEVD) is computed, from which various types of time-domain connectedness metrics are calculated. Finally, the spectral decomposition method is used to compute frequency-domain connectedness metrics and establish a link between time- and frequency-domain metrics. The empirical results of this paper, based on the sample data of China and G7 countries, reveal several important findings. The EPU of the United States acts as a net transmitter of shocks in both the short and long term, whereas China functions as a net receiver of shocks. The total connectedness index (TCI) demonstrates significant heterogeneity, with its dynamics primarily driven by short-term rather than long-term components. Additionally, connectedness shows substantial improvement under extreme conditions.