IDEAS home Printed from https://ideas.repec.org/p/cwl/cwldpp/2431.html
   My bibliography  Save this paper

Robust Inference for Time Varying Predictability: A Sieve-IVX Approach

Author

Listed:
  • Nan Liu

    (Xiamen University)

  • Yanbo Liu

    (Shandong University)

  • Peter C.B. Phillips

    (Yale University, University of Auckland, Singapore Management University)

  • Yajie Zhang

    (Singapore Management University)

Abstract

Predictive regression models are often used to evaluate the predictive capability of economic fundamentals on bond and equity returns. Inferential procedures in these regressions typically employ parameter constancy or piecewise constancy in slope coefficients. Such formulations are prone to misspecification, more especially during periods of disturbance or evolution in prevailing economic and financial conditions, which can lead to size distortion and spurious evidence of predictability. To address these issues the present work proposes a semiparametric predictive regression model with mixed-root regressors and time-varying coefficients that allow for smooth evolution in the generating mechanism over time. For estimation and inference a novel variant of the self-generated instrument approach called Sieve-IVX is introduced, giving a robust approach to inference concerning time-varying predictability that is applicable irrespective of the degrees of persistence. Asymptotic theory of the Sieve-IVX approach is provided together with both pointwise and uniform inference procedures for testing predictability and model specification. Simulations show excellent performance characteristics of these statistics in finite samples. An empirical exercise is conducted to examine excess S&P 500 returns, \ applying Sieve-IVX regression coupled with pointwise and uniform tests to reveal evidence of time-varying patterns in the predictive capability of commonly used fundamental variables.

Suggested Citation

  • Nan Liu & Yanbo Liu & Peter C.B. Phillips & Yajie Zhang, 2025. "Robust Inference for Time Varying Predictability: A Sieve-IVX Approach," Cowles Foundation Discussion Papers 2431, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:2431
    as

    Download full text from publisher

    File URL: https://cowles.yale.edu/sites/default/files/2025-03/d2431.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaohong Chen & Demian Pouzo, 2015. "Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models," Econometrica, Econometric Society, vol. 83(3), pages 1013-1079, May.
    2. Fan, Rui & Lee, Ji Hyung & Shin, Youngki, 2023. "Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach," Journal of Econometrics, Elsevier, vol. 237(2).
    3. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    4. Phillips, Peter C.B. & Lee, Ji Hyung, 2013. "Predictive regression under various degrees of persistence and robust long-horizon regression," Journal of Econometrics, Elsevier, vol. 177(2), pages 250-264.
    5. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    6. Ferson, Wayne E & Schadt, Rudi W, 1996. "Measuring Fund Strategy and Performance in Changing Economic Conditions," Journal of Finance, American Finance Association, vol. 51(2), pages 425-461, June.
    7. Liu, Yanbo & Phillips, Peter C.B., 2023. "Robust inference with stochastic local unit root regressors in predictive regressions," Journal of Econometrics, Elsevier, vol. 235(2), pages 563-591.
    8. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Kato, Kengo, 2015. "Some new asymptotic theory for least squares series: Pointwise and uniform results," Journal of Econometrics, Elsevier, vol. 186(2), pages 345-366.
    9. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.
    10. Pyun, Sungjune, 2019. "Variance risk in aggregate stock returns and time-varying return predictability," Journal of Financial Economics, Elsevier, vol. 132(1), pages 150-174.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cui, Liyuan & Hong, Yongmiao & Li, Yingxing, 2021. "Solving Euler equations via two-stage nonparametric penalized splines," Journal of Econometrics, Elsevier, vol. 222(2), pages 1024-1056.
    2. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    3. Andersen, Torben G. & Varneskov, Rasmus T., 2021. "Consistent inference for predictive regressions in persistent economic systems," Journal of Econometrics, Elsevier, vol. 224(1), pages 215-244.
    4. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.
    5. Andersen, Torben G. & Varneskov, Rasmus T., 2022. "Testing for parameter instability and structural change in persistent predictive regressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 361-386.
    6. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
    7. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    8. repec:wyi:journl:002108 is not listed on IDEAS
    9. Maynard, Alex & Shimotsu, Katsumi & Kuriyama, Nina, 2024. "Inference in predictive quantile regressions," Journal of Econometrics, Elsevier, vol. 245(1).
    10. Hidehiko Ichimura & Whitney K. Newey, 2022. "The influence function of semiparametric estimators," Quantitative Economics, Econometric Society, vol. 13(1), pages 29-61, January.
    11. Gormsen, Niels Joachim & Jensen, Christian Skov, 2024. "Conditional risk," Journal of Financial Economics, Elsevier, vol. 162(C).
    12. Buchinsky, Moshe & Li, Fanghua & Liao, Zhipeng, 2022. "Estimation and inference of semiparametric models using data from several sources," Journal of Econometrics, Elsevier, vol. 226(1), pages 80-103.
    13. Connie Becker & Wayne Ferson & David Myers & Michael Schill, 1998. "Conditional Market Timing with Benchmark Investors," NBER Working Papers 6434, National Bureau of Economic Research, Inc.
    14. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2023. "Constrained Conditional Moment Restriction Models," Econometrica, Econometric Society, vol. 91(2), pages 709-736, March.
    15. André de Souza & Anthony W. Lynch, 2012. "Does Mutual Fund Performance Vary over the Business Cycle?," NBER Working Papers 18137, National Bureau of Economic Research, Inc.
    16. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2014. "Forecasting stock returns under economic constraints," Journal of Financial Economics, Elsevier, vol. 114(3), pages 517-553.
    17. Ana Belén Alonso-Conde & Javier Rojo-Suárez, 2020. "Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    18. Ventosa-Santaulària, Daniel & Noriega, Antonio E., 2015. "Long-run monetary neutrality under stochastic and deterministic trends," Economic Modelling, Elsevier, vol. 47(C), pages 372-382.
    19. Bandi, F.M. & Perron, B. & Tamoni, A. & Tebaldi, C., 2019. "The scale of predictability," Journal of Econometrics, Elsevier, vol. 208(1), pages 120-140.
    20. Ferson, Wayne E. & Sarkissian, Sergei & Simin, Timothy, 2008. "Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(2), pages 331-353, June.
    21. Davide Pettenuzzo & Allan G. Timmermann & Rossen I. Valkanov, 2008. "Return Predictability under Equilibrium Constraints on the Equity Premium," Working Papers 37, Brandeis University, Department of Economics and International Business School.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cwl:cwldpp:2431. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Brittany Ladd (email available below). General contact details of provider: https://edirc.repec.org/data/cowleus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.