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Threshold mixed data sampling logit model with an application to forecasting US bank failures

Author

Listed:
  • Lixiong Yang

    (Lanzhou University
    Lanzhou University)

  • Mingjian Ren

    (Lanzhou University)

  • Jianming Bai

    (Lanzhou University)

Abstract

This paper introduces a threshold mixed data sampling logit (TM-logit) model, which allows for a threshold effect of independent variables sampled at different frequencies on the log-odds of dependent variable. We propose model estimation procedure and develop test statistics for relevance of high-frequency predictors, threshold effect, and equal weighting scheme. We also suggest a test statistic for the difference in forecasting accuracy between two competing models. We then extend the model to the framework with a covariate-dependent threshold (CDTM-logit) and propose estimation procedure and test statistic for threshold constancy. Monte Carlo simulations are conducted to assess the finite sample performance of the proposed estimation procedure and test statistics. The simulation results show that the estimation procedure performs well and test statistics have good size and power properties in finite samples. We apply the proposed model to predict US bank failures, and the empirical results indicate that the TM-logit and CDTM-logit models have good forecasting performance.

Suggested Citation

  • Lixiong Yang & Mingjian Ren & Jianming Bai, 2025. "Threshold mixed data sampling logit model with an application to forecasting US bank failures," Empirical Economics, Springer, vol. 68(1), pages 433-477, January.
    • Handle: RePEc:spr:empeco:v:68:y:2025:i:1:d:10.1007_s00181-024-02639-3
    DOI: 10.1007/s00181-024-02639-3
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    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. Khalaf, Lynda & Kichian, Maral & Saunders, Charles J. & Voia, Marcel, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Journal of Econometrics, Elsevier, vol. 220(2), pages 589-605.
    3. Yu, Ping, 2012. "Likelihood estimation and inference in threshold regression," Journal of Econometrics, Elsevier, vol. 167(1), pages 274-294.
    4. Seo, Myung Hwan & Linton, Oliver, 2007. "A smoothed least squares estimator for threshold regression models," Journal of Econometrics, Elsevier, vol. 141(2), pages 704-735, December.
    5. Eric Ghysels & J. Isaac Miller, 2015. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 797-816, November.
    6. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    7. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    8. Zhehao Jia & Yukun Shi & Cheng Yan & Meryem Duygun, 2020. "Bankruptcy prediction with financial systemic risk," The European Journal of Finance, Taylor & Francis Journals, vol. 26(7-8), pages 666-690, May.
    9. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    10. Noelle I. Samia & Kung-Sik Chan, 2011. "Maximum likelihood estimation of a generalized threshold stochastic regression model," Biometrika, Biometrika Trust, vol. 98(2), pages 433-448.
    11. Lixiong Yang & Chunli Zhang & Chingnun Lee & I-Po Chen, 2021. "Panel kink threshold regression model with a covariate-dependent threshold," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 462-481.
    12. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    13. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    14. Giles, Judith A & Giles, David E A, 1993. "Pre-test Estimation and Testing in Econometrics: Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 7(2), pages 145-197, June.
    15. Rebel Cole & Lawrence White, 2012. "Déjà Vu All Over Again: The Causes of U.S. Commercial Bank Failures This Time Around," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 5-29, October.
    16. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    17. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    18. Lee, Yoonseok & Wang, Yulong, 2023. "Threshold regression with nonparametric sample splitting," Journal of Econometrics, Elsevier, vol. 235(2), pages 816-842.
    19. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    20. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    21. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    22. Gupta, Jairaj & Chaudhry, Sajid, 2019. "Mind the tail, or risk to fail," Journal of Business Research, Elsevier, vol. 99(C), pages 167-185.
    23. Ping Yu & Xiaodong Fan, 2021. "Threshold Regression With a Threshold Boundary," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 953-971, October.
    24. Carmen M. Reinhart & Kenneth S. Rogoff, 2011. "From Financial Crash to Debt Crisis," American Economic Review, American Economic Association, vol. 101(5), pages 1676-1706, August.
    25. Guggenberger, Patrik, 2010. "The impact of a Hausman pretest on the size of a hypothesis test: The panel data case," Journal of Econometrics, Elsevier, vol. 156(2), pages 337-343, June.
    26. Audrino, Francesco & Kostrov, Alexander & Ortega, Juan-Pablo, 2019. "Predicting U.S. Bank Failures with MIDAS Logit Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(6), pages 2575-2603, December.
    27. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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