Jianqing Fan, Yuan Ke, Yuan Liao
arXiv 23 Mar 2016 · Statistics — Methodology · publishedJournal of Econometrics (2020) · 28 citations (OpenAlex)
arXiv:1603.07041 · PDF · DOI · OpenAlex · Extracted main text
We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama-French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the factors and loadings are identifiable up to a rotation matrix even only with a finite dimension. To incorporate the explanatory power of these covariates, we propose a smoothed principal component analysis (PCA): (i) regress the data onto the observed covariates, and (ii) take the principal components of the fitted data to estimate the loadings and factors. This allows us to accurately estimate the percentage of both explained and unexplained components in factors and thus to assess the explanatory power of covariates. We show that both the estimated factors and loadings can be estimated with improved rates of convergence compared to the benchmark method. The degree of improvement depends on the strength of the signals, representing the explanatory power of the covariates on the factors. The proposed estimator is robust to possibly heavy-tailed distributions. We apply the model to forecast US bond risk premia, and find that the observed macroeconomic characteristics contain strong explanatory powers of the factors. The gain of forecast is more substantial when the characteristics are incorporated to estimate the common factors than directly used for forecasts.
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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, J (2003) Inferential theory for factor models of large dimensions | 1.000 | 5 | 3 | 100% |
| 2 | Lam, C. and Yao, Q (2012) Factor modeling for high dimensional time-series: inference for the number of factors | 0.843 | 3 | 3 | 100% |
| 3 | Stock, J. and Watson, M (2002) Forecasting using principal components from a large number of predictors | 0.811 | 4 | 2 | 100% |
| 4 | Ahn, S. and Horenstein, A (2013) Eigenvalue ratio test for the number of factors | 0.644 | 2 | 2 | 100% |
| 5 | Fama, E. F. and French, K. R (1992) The cross-section of expected stock returns | 0.644 | 2 | 2 | 100% |
| 6 | Gibbons, M., Ross, S. and Shanken, J (1989) A test of the efficiency of a given portfolio | 0.644 | 2 | 2 | 100% |
| 7 | Huber, P (1964) Robust estimation of a location parameter | 0.644 | 2 | 2 | 100% |
| 8 | Ahn, S., Lee, Y. and Schmidt, P (2001) Gmm estimation of linear panel data models with time-varying individual effects | 0.511 | 2 | 1 | 100% |
| 9 | Moon, R. and Weidner, M (2015) Linear regression for panel with unknown number of factors as interactive fixed effects | 0.511 | 2 | 1 | 100% |
| 10 | Hart, J. D. H (1994) Automated kernel smoothing of dependent data by using time series cross- validation | 0.511 | 2 | 1 | 100% |
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