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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2605.24156 |
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| _version_ | 1866910250360111104 |
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| author | Wen, Qin Hurvich, Clifford M. |
| author_facet | Wen, Qin Hurvich, Clifford M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24156 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Long Memory in Intrinsically Dynamic Factor Models Wen, Qin Hurvich, Clifford M. Statistics Theory 62P20 G.3 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. |
| title | Long Memory in Intrinsically Dynamic Factor Models |
| topic | Statistics Theory 62P20 G.3 |
| url | https://arxiv.org/abs/2605.24156 |