arXiv 22 Jan 2020 · Econometrics · publishedJournal of Time Series Analysis (2021) · 2 citations (OpenAlex)
arXiv:2001.07949 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well-known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long-run US money demand function demonstrates the practical importance of this methodology.
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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 | Schmidt, A., Schweikert, K (2019) Multiple Structural Breaks in Cointegrating Regressions: A Model Selection Approach self | 1.000 | 7 | 4 | 100% |
| 2 | Stock, J. H., Watson, M. W (1993) A simple estimator of cointegrating vectors in higher order integrated systems | 0.928 | 4 | 3 | 100% |
| 3 | Chan, N. H., Yau, C. Y., Zhang, R.-M (2014) Group LASSO for Structural Break Time Series | 0.909 | 8 | 3 | 75% |
| 4 | Kejriwal, M., Perron, P (2008) The limit distribution of the estimates in cointegrated regression models with multiple structural changes | 0.874 | 5 | 2 | 100% |
| 5 | Harchaoui, Z., Lévy-Leduc, C (2010) Multiple Change-Point Estimation With a Total Variation Penalty | 0.843 | 4 | 3 | 75% |
| 6 | Gregory, A. W., Hansen, B. E (1996) a | 0.843 | 5 | 4 | 60% |
| 7 | Bai, J., Lumsdaine, R. L., Stock, J. H (1998) Testing for and Dating Common Breaks in Multivariate Time Series | 0.843 | 3 | 3 | 100% |
| 8 | Maki, D (2012) Tests for cointegration allowing for an unknown number of breaks | 0.811 | 4 | 2 | 100% |
| 9 | Kejriwal, M., Perron, P (2010) Testing for Multiple Structural Changes in Cointegrated Regression Models | 0.737 | 3 | 2 | 100% |
| 10 | Qian, J., Su, L (2016) Shrinkage Estimation of Regression Models With Multiple Structural Changes | 0.737 | 3 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.