Feiyu Jiang, Zifeng Zhao, Xiaofeng Shao
arXiv 9 Jul 2020 · Econometrics · publishedJournal of Econometrics (2020) · 83 citations (OpenAlex)
arXiv:2007.04553 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.
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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 | Baranowski, R., Y. Chen, and P. Fryzlewicz (2019) Narrowest-over-threshold detection of multiple change points and change-point-like features | 1.000 | 5 | 3 | 100% |
| 2 | Bai, J. and P. Perron (1998) Estimating and testing linear models with multiple structural changes | 0.843 | 3 | 3 | 100% |
| 3 | Shao, X (2010) A self-normalized approach to confidence interval construction in time series self | 0.843 | 3 | 3 | 100% |
| 4 | Andrews, D. W (1993) Tests for parameter instability and structural change with unknown change point | 0.737 | 3 | 2 | 100% |
| 5 | Shao, X. and X. Zhang (2010) Testing for change points in time series self | 0.644 | 4 | 1 | 100% |
| 6 | Fryzlewicz, P (2014) Wild binary segmentation for multiple change-point detection | 0.644 | 2 | 2 | 100% |
| 7 | Shao, X (2015) Self-normalization for time series: a review of recent developments self | 0.644 | 2 | 2 | 100% |
| 8 | Bauwens, L., G. Koop, D. Korobilis, and J. V. Rombouts (2015) The contribution of structural break models to forecasting macroeconomic series | 0.405 | 1 | 1 | 100% |
| 9 | Cho, H. and P. Fryzlewicz (2015) Multiple change-point detection for high-dimensional time series via sparsified binary segmentation | 0.405 | 1 | 1 | 100% |
| 10 | Fan, Z. and L. Mackey (2017) An empirical bayesian analysis of simultaneous changepoints in multiple data sequences | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Short-Term Covid-19 Forecast for Latecomers | 0.405 | 1 | 1 |
| 2 | Detecting long-range dependence for time-varying linear models | 0.000 | 1 | 1 |