arXiv 7 Jan 2022 · Econometrics
arXiv:2201.02532 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel approximate factor model tailored for analyzing time-dependent curve data. Our model decomposes such data into two distinct components: a low-dimensional predictable factor component and an unpredictable error term. These components are identified through the autocovariance structure of the underlying functional time series. The model parameters are consistently estimated using the eigencomponents of a cumulative autocovariance operator and an information criterion is proposed to determine the appropriate number of factors. Applications to mortality and yield curve modeling illustrate key advantages of our approach over the widely used functional principal component analysis, as it offers parsimonious structural representations of the underlying dynamics along with gains in out-of-sample forecast performance.
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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 | Aue, A., Norinho, D. D., and Hörmann, S (2015) On the prediction of stationary functional time series | 1.000 | 6 | 3 | 100% |
| 2 | Diebold, F. X. and Li, C (2006) Forecasting the term structure of government bond yields | 0.644 | 4 | 1 | 100% |
| 3 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.644 | 2 | 2 | 100% |
| 4 | Bathia, N., Yao, Q., and Ziegelmann, F (2010) Identifying the finite dimensionality of curve time series | 0.644 | 2 | 2 | 100% |
| 5 | Diebold, F. X. and Rudebusch, G. D (2013) Yield curve modeling and forecasting: The dynamic Nelson-Siegel approach | 0.644 | 2 | 2 | 100% |
| 6 | Hays, S., Shen, H., and Huang, J. Z (2012) Functional dynamic factor models with application to yield curve forecasting | 0.644 | 2 | 2 | 100% |
| 7 | Kokoszka, P. and Reimherr, M (2017) Introduction to Functional Data Analysis | 0.644 | 2 | 2 | 100% |
| 8 | Lam, C. and Yao, Q (2012) Factor modeling for high-dimensional time series: Inference for the number of factors | 0.644 | 2 | 2 | 100% |
| 9 | Ramsay, J. and Silverman, B (2005) Functional data analysis | 0.644 | 2 | 2 | 100% |
| 10 | Stock, J. H. and Watson, M. W (2002) Forecasting using principal components from a large number of predictors | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 60 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2503.12611 | 0.928 | 4 | 3 |
| 2 | Dynamic Matrix Factor Models for High Dimensional Time Series | 0.405 | 1 | 1 |
| 3 | Threshold Tensor Factor Model in CP Form | 0.405 | 1 | 1 |