arXiv 28 Sep 2024 · Econometrics
arXiv:2409.19287 · PDF · DOI · OpenAlex · Extracted main text
The modal factor model represents a new factor model for dimension reduction in high dimensional panel data. Unlike the approximate factor model that targets for the mean factors, it captures factors that influence the conditional mode of the distribution of the observables. Statistical inference is developed with the aid of mode estimation, where the modal factors and the loadings are estimated through maximizing a kernel-type objective function. An easy-to-implement alternating maximization algorithm is designed to obtain the estimators numerically. Two model selection criteria are further proposed to determine the number of factors. The asymptotic properties of the proposed estimators are established under some regularity conditions. Simulations demonstrate the nice finite sample performance of our proposed estimators, even in the presence of heavy-tailed and asymmetric idiosyncratic error distributions. Finally, the application to inflation forecasting illustrates the practical merits of modal factors.
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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 | 6 | 4 | 100% |
| 2 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.977 | 15 | 6 | 93% |
| 3 | Kemp, G. C. and Silva, J. S (2012) Regression towards the mode | 0.977 | 15 | 4 | 93% |
| 4 | Yao, W. and Li, L (2014) A new regression model: modal linear regression | 0.976 | 14 | 5 | 93% |
| 5 | Chen, L., Dolado, J. J., and Gonzalo, J (2021) Quantile factor models | 0.963 | 28 | 7 | 89% |
| 6 | Wang, F (2022) Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented… | 0.928 | 4 | 3 | 100% |
| 7 | Ullah, A., Wang, T., and Yao, W (2021) Modal regression for fixed effects panel data | 0.737 | 3 | 2 | 100% |
| 8 | Kemp, G. C., Parente, P. M., and Santos Silva, J (2020) Dynamic vector mode regression | 0.644 | 4 | 1 | 100% |
| 9 | Masry, E (1996) Multivariate regression estimation local polynomial fitting for time series | 0.644 | 3 | 2 | 67% |
| 10 | Bai, J. and Li, K (2016) Maximum likelihood estimation and inference for approximate factor models of high dimension | 0.644 | 2 | 2 | 100% |
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