Inference in matrix-valued time series with common stochastic trends and multifactor error structure
Rong Chen; Simone Giannerini; Greta Goracci; Lorenzo Trapani · 2025 · arXiv
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Abstract (excerpt)
We develop an estimation methodology for a factor model for high-dimensional matrix-valued time series, where common stochastic trends and common stationary factors can be present. We study, in particular, the estimation of (row and column) loading spaces, of the common stochastic trends and of the common stationary factors, and the row and column ranks thereof. In a set of (negative) preliminary results, we show that a projection-based technique fails to improve the rates of convergence compared to a "flattened" estimation technique which does not take into account the matrix nature of the da
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Metadata source: arXiv