Authors:Qin Wen (Stern School, New York University), Clifford M. Hurvich (Stern School, New York University)
Abstract:We study the generalized dynamic factor model in a long-memory setting. Unlike most recent work, which assumes a finite-dimensional factor space and short memory, our framework allows the factor space to be infinite-dimensional and the common components to exhibit long memory. We employ the two-sided estimation method of Forni, Hallin, Lippi and Reichlin (2000, Review of Economics and Statistics) to recover the common component. The long memory structure of the common component poses a challenge, as it introduces unboundedness/discontinuity in the spectral density. We address this issue by leveraging two key facts: First, the estimated operator is a projection onto the leading eigenspace and thus the eigengap provides an intrinsic scaling that partially mitigates the blow-up. Second, we perform most of our estimation in $L^p$-norm, rather than pointwise. Experimental results are presented to provide evidence supporting the theory, as well as potential improvements to it.
| Comments: | 92 pages, 11 figures |
| Subjects: | Statistics Theory (math.ST) |
| MSC classes: | 62P20 |
| ACM classes: | G.3 |
| Cite as: | arXiv:2605.24156 [math.ST] |
| (or arXiv:2605.24156v1 [math.ST] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24156 arXiv-issued DOI via DataCite (pending registration) |
From: Clifford Hurvich [view email]
[v1]
Fri, 22 May 2026 19:24:09 UTC (1,387 KB)
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