Binary response models for heterogeneous panel data with interactive fixed effects

Jiti Gao; Fei Liu; Bin Peng; Yayi Yan

doi:10.1016/j.jeconom.2023.01.009

Binary response models for heterogeneous panel data with interactive fixed effects

Jiti Gao, Fei Liu, Bin Peng, Yayi Yan

Department of Economics

Research output: Contribution to journal › Article › peer-review

7 Citations (SciVal)

Abstract

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and establish the corresponding asymptotic theory. In addition, we conduct intensive simulations to examine the theoretical findings. In an empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis.

Original language	English
Pages (from-to)	1654-1679
Number of pages	26
Journal	Journal of Econometrics
Volume	235
Issue number	2
Early online date	1 Feb 2023
DOIs	https://doi.org/10.1016/j.jeconom.2023.01.009
Publication status	Published - 31 Aug 2023

Acknowledgements

We would like to thank the Co-Editor Professor Elie Tamer, the Associate Editor and three referees for their constructive comments on the original submission. The authors of this paper would like to thank many online seminar and conference participants in the years of 2020 and 2021 for their constructive comments on earlier versions of this paper.

Funding

Thanks from Gao and Peng also go to the Australian Research Council Discovery Grants Program for its financial support under Grant Numbers: DP200102769 and DP210100476. Liu’s research is financially supported by the Fundamental Research Funds for the Central Universities, China (No. 63212145) and National Natural Science Foundation of China under Grant No. 72203114.

Keywords

Binary response
Heterogeneous panel
Interactive fixed effects
Portfolio analysis

ASJC Scopus subject areas

Economics and Econometrics
Applied Mathematics

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10.1016/j.jeconom.2023.01.009

Cite this

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abstract = "In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and establish the corresponding asymptotic theory. In addition, we conduct intensive simulations to examine the theoretical findings. In an empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis. ",

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AB - In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and establish the corresponding asymptotic theory. In addition, we conduct intensive simulations to examine the theoretical findings. In an empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis.

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Binary response models for heterogeneous panel data with interactive fixed effects

Abstract

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Keywords

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