arXiv 7 Feb 2022 · Econometrics
arXiv:2202.02988 · PDF · DOI · OpenAlex · Extracted main text
This article proposes an estimation method to detect breakpoints for linear time series models with their parameters that jump scarcely. Its basic idea owes the group LASSO (group least absolute shrinkage and selection operator). The method practically provides estimates of such time-varying parameters of the models. An example shows that our method can detect each structural breakpoint's date and magnitude.
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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 | Ito, M., Noda, A., and Wada, T (2021) Time-Varying Comovement of Foreign Exchange Markets: A GLS-Based Time-Varying Model Approach self | 0.874 | 8 | 2 | 100% |
| 2 | Ito, M (2019) An Estimation Method for State Space Models using generalized LASSO Techniques, Reported in 94th annual conference of WEAI self | 0.644 | 2 | 2 | 100% |
| 3 | Basu, S., Michailidis, G., et al (2015) Regularized estimation in sparse high-dimensional time series models | 0.405 | 1 | 1 | 100% |
| 4 | Chan, N. H., Yau, C. Y., and Zhang, R.-M (2014) Group LASSO for structural break time series | 0.405 | 1 | 1 | 100% |
| 5 | Michailidis, G. and d’Alché Buc, F (2013) Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues | 0.405 | 1 | 1 | 100% |
| 6 | Swamy, P., Tavlas, G. S., Hall, S. G., and Hondroyiannis, G (2010) Estimation of parameters in the presence of model misspecification and measurement error | 0.405 | 1 | 1 | 100% |
| 7 | Tibshirani, R. J (2011) The solution path of the generalized lasso | 0.405 | 1 | 1 | 100% |
| 8 | Tibshirani, R., Wainwright, M., and Hastie, T (2015) Statistical learning with sparsity: the lasso and generalizations | 0.405 | 1 | 1 | 100% |
| 9 | Yuan, M. and Lin, Y (2006) Model selection and estimation in regression with grouped variables | 0.405 | 1 | 1 | 100% |
Showing the top 9 of 9 scored citations.