Chaohua Dong, Jiti Gao, Oliver Linton, Bin Peng
arXiv 19 Jun 2020 · Econometrics
arXiv:2006.11060 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we study the trending behaviour of COVID-19 data at country level, and draw attention to some existing econometric tools which are potentially helpful to understand the trend better in future studies. In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic elsewhere. Africa and America are still facing serious challenges in terms of managing the spread of the virus, and reducing the death rate, although in Africa the virus spreads slower and has a lower death rate than the other regions. By comparing the performances of different countries, our results incidentally agree with Gu et al. (2020), though different approaches and models are considered. For example, both works agree that countries such as USA, UK and Italy perform relatively poorly; on the other hand, Australia, China, Japan, Korea, and Singapore perform relatively better.
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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 | Gu, Yan, Huang, Zhu, Sun, Zhang, Wang, Qiu \ Chen (2020) Better strategies for containing COVID-19 epidemics –- A study of 25 countries via an extended varying coefficient seir model | 1.000 | 6 | 4 | 100% |
| 2 | Gao, Linton \ Peng (2020) `Inference on a semiparametric model with global power law and local nonparametric trends', Econometric Theory 36(2), 223–249 | 0.811 | 4 | 2 | 100% |
| 3 | Li \ Linton (2020) When will the Covid-19 pandemic peak? Working paper available at https://www.inet.econ.cam.ac.uk/research-papers/wp-abstracts?wp… | 0.644 | 2 | 2 | 100% |
| 4 | Su \ Wang (2017) `On time-varying factor models: Estimation and testing', Journal of Econometrics 198(1), 84–101 | 0.644 | 2 | 2 | 100% |
| 5 | Bai \ Ng (2019) Matrix completion, counterfactuals, and factor analysis of missing data | 0.511 | 2 | 1 | 100% |
| 6 | Su, Miao \ Jin (2019) On factor models with random missing: Em estimation,inference, and cross validation | 0.511 | 2 | 1 | 100% |
| 7 | Bai \ Ng (2002) `Determining the number of factors in approximate factor models', Econometrica 70(1), 191–221 | 0.405 | 1 | 1 | 100% |
| 8 | Hong \ Li (2005) `Nonparametric specification testing for continuous-time models with applications to term structure of interest rates', The Revi… | 0.405 | 1 | 1 | 100% |
| 9 | Liu, Moon \ Schorfheide (2020) Panel forecasts of country-level covid-19 infections | 0.405 | 1 | 1 | 100% |
| 10 | Li \ Racine (2006) Nonparametric Econometrics Theory and Practice, Princeton University Press | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 14 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Time-Varying Poisson Autoregression | 0.405 | 1 | 1 |