Jiangtao Duan, Jushan Bai, Xu Han
arXiv 25 Feb 2021 · Econometrics · publishedJournal of Econometrics (2022) · 1 citations (OpenAlex)
arXiv:2102.12666 · PDF · DOI · OpenAlex · Extracted main text
This paper estimates the break point for large-dimensional factor models with a single structural break in factor loadings at a common unknown date. First, we propose a quasi-maximum likelihood (QML) estimator of the change point based on the second moments of factors, which are estimated by principal component analysis. We show that the QML estimator performs consistently when the covariance matrix of the pre- or post-break factor loading, or both, is singular. When the loading matrix undergoes a rotational type of change while the number of factors remains constant over time, the QML estimator incurs a stochastically bounded estimation error. In this case, we establish an asymptotic distribution of the QML estimator. The simulation results validate the feasibility of this estimator when used in finite samples. In addition, we demonstrate empirical applications of the proposed method by applying it to estimate the break points in a U.S. macroeconomic dataset and a stock return dataset.
appendix boundary found by appendix_titled_section at “Appendix” · 38% of the source is main text. Read the extracted text to check this.
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 | Bai, J., Han, X., Shi, Y (2020) Estimation and inference of change points in high-dimensional factor models self | 1.000 | 12 | 5 | 100% |
| 2 | Ma, S., Su, L (2018) Estimation of large dimensional factor models with an unknown number of breaks | 1.000 | 5 | 3 | 100% |
| 3 | Baltagi, B., Kao, C., Wang, F (2017) Identification and estimation of a large factor model with structural instability | 0.956 | 8 | 5 | 88% |
| 4 | Bai, J., Ng, S (2002) Determining the number of factors in approximate factor models self | 0.928 | 4 | 3 | 100% |
| 5 | Cheng, X., Liao, Z., Schorfheide, F (2016) Shrinkage estimation of high-Dimensional factor models with structural instabilities | 0.811 | 4 | 2 | 100% |
| 6 | Barigozzi, M., Cho, H., Fryzlewicz, P (2018) Simultaneous multiple change-point andfactor analysis for high-dimensional time series | 0.737 | 3 | 2 | 100% |
| 7 | Bai, J (2003) Inferential theory for factor models of large dimensions self | 0.585 | 4 | 3 | 25% |
| 8 | Bai, J (1997) Estimation Of A Change Point In Multiple Regression Models self | 0.511 | 2 | 1 | 100% |
| 9 | Qu, Z., Perron, P (2007) Estimating and testing structural changes in multivariate regressions | 0.511 | 2 | 1 | 100% |
| 10 | Ahn, S., Horenstein, A (2013) Eigenvalue ratio test for the number of factors | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 46 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.