arXiv 8 Sep 2021 · Econometrics · publishedJournal of Econometrics (2023) · 84 citations (OpenAlex)
arXiv:2109.03773 · PDF · DOI · OpenAlex · Extracted main text
Pervasive cross-section dependence is increasingly recognized as a characteristic of economic data and the approximate factor model provides a useful framework for analysis. Assuming a strong factor structure where $\Lop\Lo/N^\alpha$ is positive definite in the limit when $\alpha=1$, early work established convergence of the principal component estimates of the factors and loadings up to a rotation matrix. This paper shows that the estimates are still consistent and asymptotically normal when $\alpha\in(0,1]$ albeit at slower rates and under additional assumptions on the sample size. The results hold whether $\alpha$ is constant or varies across factor loadings. The framework developed for heterogeneous loadings and the simplified proofs that can be also used in strong factor analysis are of independent interest.
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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 and Ng (2002) Determining the Number of Factors in Approximate Factor Models | 1.000 | 5 | 4 | 100% |
| 2 | Onatski (2012) Asymptotics of the Principal Components Estimator of Large Factor Models with Weakly Influential Factors | 1.000 | 5 | 3 | 100% |
| 3 | Bai (2003) Inferential Theory for Factor Models of Large Dimensions self | 0.928 | 4 | 3 | 100% |
| 4 | Freyaldenhoven (2022) Factor Models with Local Factors: Determining the Number of Relevant Factors | 0.928 | 4 | 3 | 100% |
| 5 | Uematsu and Yamagata (2022) Estimation of Sparsity Induced Weak Factor Models | 0.928 | 4 | 3 | 100% |
| 6 | Bai and Ng (2019) Rank Regularized Estimation of Approximate Factor Models | 0.644 | 2 | 2 | 100% |
| 7 | DeMol, Giannone, and Reichlin (2008) Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components? | 0.644 | 2 | 2 | 100% |
| 8 | Stock and Watson (1998) Diffusion Indexes | 0.644 | 2 | 2 | 100% |
| 9 | Stock and Watson (2002) Forecasting Using Principle Components from a Large Number of Predictors | 0.644 | 2 | 2 | 100% |
| 10 | Bai and Ng (2006) Confidence Intervals for Diffusion Index Forecasts and Inference with Factor-Augmented Regressions | 0.585 | 3 | 1 | 100% |
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