Samuel N. Cohen, Silvia Lui, Will Malpass, Giulia Mantoan, Lars Nesheim, Áureo de Paula, Andrew Reeves, Craig Scott, Emma Small, Lingyi Yang
arXiv 17 May 2023 · Econometrics · 3 citations (OpenAlex)
arXiv:2305.10256 · PDF · DOI · OpenAlex · Extracted main text
Key economic variables are often published with a significant delay of over a month. The nowcasting literature has arisen to provide fast, reliable estimates of delayed economic indicators and is closely related to filtering methods in signal processing. The path signature is a mathematical object which captures geometric properties of sequential data; it naturally handles missing data from mixed frequency and/or irregular sampling -- issues often encountered when merging multiple data sources -- by embedding the observed data in continuous time. Calculating path signatures and using them as features in models has achieved state-of-the-art results in fields such as finance, medicine, and cyber security. We look at the nowcasting problem by applying regression on signatures, a simple linear model on these nonlinear objects that we show subsumes the popular Kalman filter. We quantify the performance via a simulation exercise, and through application to nowcasting US GDP growth, where we see a lower error than a dynamic factor model based on the New York Fed staff nowcasting model. Finally we demonstrate the flexibility of this method by applying regression on signatures to nowcast weekly fuel prices using daily data. Regression on signatures is an easy-to-apply approach that allows great flexibility for data with complex sampling patterns.
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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 | Bok, Brandyn and Caratelli, Daniele and Giannone, Domenico and Sbord… (2018) Macroeconomic nowcasting and forecasting with big data | 0.974 | 13 | 5 | 92% |
| 2 | Levin, Daniel and Lyons, Terry and Ni, Hao (2013) Learning from the past, predicting the statistics for the future, learning an evolving system | 0.928 | 5 | 3 | 80% |
| 3 | Jeremy Reizenstein and Benjamin Graham (2020) Algorithm 1004: The iisignature Library: Efficient Calculation of Iterated-Integral Signatures and Log Signatures | 0.843 | 3 | 3 | 100% |
| 4 | Morrill, James and Kidger, Patrick and Yang, Lingyi and Lyons, Terry (2022) On the Choice of Interpolation Scheme for Neural CDEs self | 0.811 | 4 | 2 | 100% |
| 5 | Lyons, Terry J and Caruana, Michael and Lévy, Thierry (2007) Differential Equations Driven by Rough Paths | 0.737 | 3 | 2 | 100% |
| 6 | Giannone, Domenico and Reichlin, Lucrezia and Small, David H (2006) Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases | 0.644 | 2 | 2 | 100% |
| 7 | A. P. Dempster and N. M. Laird and D. B. Rubin (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm | 0.644 | 2 | 2 | 100% |
| 8 | Graham, Benjamin (2013) Sparse arrays of signatures for online character recognition | 0.644 | 2 | 2 | 100% |
| 9 | Morrill, James H and Kormilitzin, Andrey and Nevado-Holgado, Alejo J… (2020) Utilization of the Signature Method to Identify the Early Onset of Sepsis From Multivariate Physiological Time Series in Critica… | 0.644 | 2 | 2 | 100% |
| 10 | Morrill, James and Salvi, Cristopher and Kidger, Patrick and Foster,… (2021) Neural Rough Differential Equations for Long Time Series | 0.644 | 2 | 2 | 100% |
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