Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series
降采样与混频时间序列中结构向量自回归模型的可识别性与估计
Summary Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector…