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High Dimensional Discrete Choice Models With Interactive Fixed Effects Applied to Causal Inference

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  • Ye Chen
  • Ke Miao
  • Liangjun Su

Abstract

We propose a two‐step procedure to estimate a high dimensional discrete choice panel with interactive fixed effects where the initial and final estimators are obtained via a nuclear‐norm regularized (NNR) maximum likelihood estimation and post‐NNR iterated estimation, respectively. We apply the method to make counterfactual predictions of choice probabilities. Simulations demonstrate nice finite sample performance in estimation and tests. An illustrative application highlights the practical usefulness of our approach, revealing that the stock return of Fantasia Holdings Group Company Limited did experience a significant directional change following the 2021 credit rating downgrade event.

Suggested Citation

  • Ye Chen & Ke Miao & Liangjun Su, 2026. "High Dimensional Discrete Choice Models With Interactive Fixed Effects Applied to Causal Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(1), pages 108-126, January.
  • Handle: RePEc:wly:japmet:v:41:y:2026:i:1:p:108-126
    DOI: 10.1002/jae.70019
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    1. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    2. Jérôme Hubler & Christine Louargant & Patrice Laroche & Jean‐Noёl Ory, 2019. "How Do Rating Agencies’ Decisions Impact Stock Markets? A Meta‐Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 33(4), pages 1173-1198, September.
    3. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    4. Boneva, L. & Linton, O., 2017. "A Discrete Choice Model For Large Heterogeneous Panels with Interactive Fixed Effects with an Application to the Determinants of Corporate Bond Issuance," Cambridge Working Papers in Economics 1703, Faculty of Economics, University of Cambridge.
    5. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    6. Norden, Lars & Weber, Martin, 2004. "Informational efficiency of credit default swap and stock markets: The impact of credit rating announcements," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2813-2843, November.
    7. Jushan Bai & Serena Ng, 2021. "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1746-1763, October.
    8. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    9. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    10. Andrew Skabar, 2013. "Direction‐of‐Change Financial Time Series Forecasting using a Similarity‐Based Classification Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 409-422, August.
    11. Schertler, Andrea & Moch, Nils, 2021. "Bank foreign assets, government support and international spillover effects of sovereign rating events on bank stock prices," Journal of Banking & Finance, Elsevier, vol. 130(C).
    12. Su, Liangjun & Wang, Fa, 2025. "Inference for large dimensional factor models under general missing data patterns," Journal of Econometrics, Elsevier, vol. 250(C).
    13. Linton, O. & Whang, Yoon-Jae, 2007. "The quantilogram: With an application to evaluating directional predictability," Journal of Econometrics, Elsevier, vol. 141(1), pages 250-282, November.
    14. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    15. Anatolyev, Stanislav & Baruník, Jozef, 2019. "Forecasting dynamic return distributions based on ordered binary choice," International Journal of Forecasting, Elsevier, vol. 35(3), pages 823-835.
    16. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    17. Lena Boneva & Oliver Linton, 2017. "A discrete†choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1226-1243, November.
    18. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    19. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    20. Kim, Yongtae & Nabar, Sandeep, 2007. "Bankruptcy probability changes and the differential informativeness of bond upgrades and downgrades," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3843-3861, December.
    21. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578.
    22. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578, April.
    23. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
    24. Caggiano, Giovanni & Calice, Pietro & Leonida, Leone, 2014. "Early warning systems and systemic banking crises in low income countries: A multinomial logit approach," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 258-269.
    25. Carro, Jesus M., 2007. "Estimating dynamic panel data discrete choice models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 503-528, October.
    26. Bai, Jushan & Ng, Serena, 2019. "Rank regularized estimation of approximate factor models," Journal of Econometrics, Elsevier, vol. 212(1), pages 78-96.
    27. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    28. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    29. Anatolyev, Stanislav & Gospodinov, Nikolay, 2010. "Modeling Financial Return Dynamics via Decomposition," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 232-245.
    30. Wang, Fa, 2022. "Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions," Journal of Econometrics, Elsevier, vol. 229(1), pages 180-200.
    31. Sumit Agarwal & Vincent Y. S. Chen & Weina Zhang, 2016. "The Information Value of Credit Rating Action Reports: A Textual Analysis," Management Science, INFORMS, vol. 62(8), pages 2218-2240, August.
    32. Bester, C. Alan & Hansen, Christian, 2009. "Identification of Marginal Effects in a Nonparametric Correlated Random Effects Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 235-250.
    33. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    34. L. Fiévet & D. Sornette, 2018. "Decision trees unearth return sign predictability in the S&P 500," Quantitative Finance, Taylor & Francis Journals, vol. 18(11), pages 1797-1814, November.
    35. Gao, Jiti & Liu, Fei & Peng, Bin & Yan, Yayi, 2023. "Binary response models for heterogeneous panel data with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 235(2), pages 1654-1679.
    36. Fan, Jianqing & Fan, Yingying & Lv, Jinchi, 2008. "High dimensional covariance matrix estimation using a factor model," Journal of Econometrics, Elsevier, vol. 147(1), pages 186-197, November.
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