arXiv 6 Jul 2018 · Econometrics · publishedJournal of Business and Economic Statistics (2018) · 7 citations (OpenAlex)
arXiv:1807.02248 · PDF · DOI · OpenAlex · Extracted main text
This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state process. We develop an estimator for the latent factors and state-varying loadings under a large cross-section and time dimension. Our estimator combines nonparametric methods with principal component analysis. We derive the rate of convergence and limiting normal distribution for the factors, loadings and common components. In addition, we develop a statistical test for a change in the factor structure in different states. We apply the estimator to U.S. Treasury yields and S&P500 stock returns. The systematic factor structure in treasury yields differs in times of booms and recessions as well as in periods of high market volatility. State-varying factors based on the VIX capture significantly more variation and pricing information in individual stocks than constant factor models.
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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 | Bai (2003) Inferential theory for factor models of large dimensions | 1.000 | 18 | 7 | 100% |
| 2 | Bai and Ng (2002) Determining the number of factors in approximate factor models | 1.000 | 15 | 8 | 100% |
| 3 | Su and Wang (2017) On time-varying factor models: Estimation and testing | 1.000 | 14 | 7 | 100% |
| 4 | Bai, Han, and Shi (2020) Estimation and inference of change points in high-dimensional factor models | 1.000 | 5 | 3 | 100% |
| 5 | Lettau and Pelger (2020) Factors that Fit the Time-Series and Cross-Section of Stock Returns | 1.000 | 5 | 3 | 100% |
| 6 | Pelger (2020) Understanding Systematic Risk: A High-Frequency Approach self | 0.928 | 4 | 3 | 100% |
| 7 | Baltagi, Kao, and Wang (2020) Estimating and testing high dimensional factor models with multiple structural changes | 0.874 | 6 | 2 | 100% |
| 8 | Park, Mammen, Härdle, and Borak (2009) Time Series Modelling With Semiparametric Factor Dynamics | 0.811 | 4 | 2 | 100% |
| 9 | Cheng, Liao, and Schorfheide (2016) Shrinkage estimation of high-dimensional factor models with structural instabilities | 0.737 | 3 | 2 | 100% |
| 10 | Fan, Liao, and Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.737 | 3 | 2 | 100% |
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