Tingting Cheng, Jiachen Cong, Fei Liu, Xuanbin Yang
arXiv 22 Jul 2025 · Econometrics
arXiv:2507.16462 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we propose a novel factor-augmented forecasting regression model with a binary response variable. We develop a maximum likelihood estimation method for the regression parameters and establish the asymptotic properties of the resulting estimators. Monte Carlo simulation results show that the proposed estimation method performs very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through an application to U.S. recession forecasting. The proposed model consistently outperforms conventional Probit regression across both in-sample and out-of-sample exercises, by effectively utilizing high-dimensional information through latent 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. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.894 | 7 | 3 | 71% |
| 2 | Bai, J. and S. Ng (2006) Confidence intervals for diffusion index forecasts and inference for factor-augmented regressions | 0.874 | 7 | 2 | 100% |
| 3 | Gao, J., F. Liu, B. Peng, and Y. Yan (2023) Binary response models for heterogeneous panel data with interactive fixed effects | 0.843 | 4 | 3 | 75% |
| 4 | Christiansen, C., J. N. Eriksen, and S. V. Mller (2014) Forecasting us recessions: The role of sentiment | 0.737 | 3 | 2 | 100% |
| 5 | Kauppi, H. and P. Saikkonen (2008) Predicting us recessions with dynamic binary response models | 0.737 | 3 | 2 | 100% |
| 6 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.693 | 6 | 2 | 50% |
| 7 | Hannadige, S. B., J. Gao, M. J. Silvapulle, and P. S. and (2024) Forecasting a nonstationary time series using a mixture of stationary and nonstationary factors as predictors | 0.644 | 4 | 1 | 100% |
| 8 | Chen, Q., Y. Hong, and H. Li (2024) Time-varying forecast combination for factor-augmented regressions with smooth structural changes | 0.644 | 2 | 2 | 100% |
| 9 | Estrella, A. and F. S. Mishkin (1998) Predicting us recessions: Financial variables as leading indicators | 0.644 | 2 | 2 | 100% |
| 10 | Yan, Y. and T. Cheng (2022) Factor-augmented forecasting regressions with threshold effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 63 scored citations.