arXiv 17 Jan 2026 · Econometrics
arXiv:2601.12039 · PDF · DOI · OpenAlex · Extracted main text
The paper develops a Transformer architecture for estimating dynamic factors from multivariate time series data under flexible identification assumptions. Performance on small datasets is improved substantially by using a conventional factor model as prior information via a regularization term in the training objective. The results are interpreted with Attention matrices that quantify the relative importance of variables and their lags for the factor estimate. Time variation in Attention patterns can help detect regime switches and evaluate narratives. Monte Carlo experiments suggest that the Transformer is more accurate than the linear factor model, when the data deviate from linear-Gaussian assumptions. An empirical application uses the Transformer to construct a coincident index of U.S. real economic activity.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Stock, J.H. and M. Watson (1989) New Indexes of Coincident and Leading Economic Indicators | 0.874 | 8 | 2 | 100% |
| 2 | Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Ja… (2017) Attention Is All You Need | 0.874 | 6 | 2 | 100% |
| 3 | Fernández-Villaverde, Jesús and Rubio-Ram\'irez, Juan F (2005) Estimating Dynamic Equilibrium Economies: Linear Versus Nonlinear Likelihood | 0.644 | 2 | 2 | 100% |
| 4 | Kalman, Rudolph Emil (1960) A New Approach to Linear Filtering and Prediction Problems | 0.644 | 2 | 2 | 100% |
| 5 | Särkkä, Simo and Svensson, Lennart (2023) Bayesian Filtering and Smoothing | 0.644 | 2 | 2 | 100% |
| 6 | Haoyi Zhou and Shanghang Zhang and Jieqi Peng and Shuai Zhang and Ji… (2021) Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting | 0.585 | 3 | 1 | 100% |
| 7 | Hochreiter, Sepp and Schmidhuber, Jurgen (1997) Long Short-Term Memory | 0.511 | 2 | 1 | 100% |
| 8 | Binh Tang and David S. Matteson (2021) Probabilistic Transformer For Time Series Analysis | 0.511 | 2 | 1 | 100% |
| 9 | Jean-Baptiste Cordonnier and Andreas Loukas and Martin Jaggi (2021) Multi-Head Attention: Collaborate Instead of Concatenate | 0.405 | 1 | 1 | 100% |
| 10 | Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kri… (2018) BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 54 scored citations.