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The Dynamic, the Static, and the Weak: Factor models and the analysis of high-dimensional time series

Matteo Barigozzi, Marc Hallin

arXiv 15 Jul 2024 · Econometrics · publishedJournal of Time Series Analysis (2025) · 5 citations (OpenAlex)

arXiv:2407.10653 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Several fundamental and closely interconnected issues related to factor models are reviewed and discussed: dynamic versus static loadings, rate-strong versus rate-weak factors, the concept of weakly common component recently introduced by Gersing et al. (2023), the irrelevance of cross-sectional ordering and the assumption of cross-sectional exchangeability, the impact of undetected strong factors, and the problem of combining common and idiosyncratic forecasts. Conclusions all point to the advantages of the General Dynamic Factor Model approach of Forni et al. (2000) over the widely used Static Approximate Factor Model introduced by Chamberlain and Rothschild (1983).

Citation extraction

81
references
183
in-text mentions
81
distinct cited
13
self-citations
18,802
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Chamberlain, G. and M. Rothschild (1983) Arbitrage, factor structure, and mean-variance analysis on large asset markets1.000138100%
2Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2000) The generalized dynamic-factor model: Identification and estimation1.000104100%
3Chamberlain, G (1983) Funds, factors and diversification in arbitrage pricing models1.00086100%
4Stock, J. H. and M. W. Watson (2002) Forecasting using principal components from a large number of predictors1.00065100%
5Onatski, A (2012) Asymptotics of the principal components estimator of large factor models with weakly influential factors1.00064100%
6Gersing, P., C. Rust, and M. Deistler (2023) Weak factors are everywhere1.00063100%
7Bai, J (2003) Inferential theory for factor models of large dimensions1.00055100%
8Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models1.00054100%
9Hallin, M. and R. Liska (2007) The generalized dynamic factor model:\ determining the number of factors self0.87482100%
10Forni, M. and M. Lippi (2001) The generalized dynamic factor model: representation theory0.87472100%

Showing the top 10 of 81 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Principal Component Analysis .3cm for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification0.73732
2New Tests of Equal Forecast Accuracy for Factor-Augmented Regressions with Weaker Loadings0.64422
3On the Existence of One-Sided Representations for the Generalised Dynamic Factor Model0.51122
4A Distributed Lag Approach to the Generalised Dynamic Factor Model0.51121
5The Canonical Decomposition of Factor Models: Weak Factors are Everywhere0.40511
6Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?0.40511