IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v5y2007i4p523-559.html
   My bibliography  Save this article

A Statistical Inquiry into the Plausibility of Recursive Utility

Author

Listed:
  • Han Hong

Abstract

We use purely statistical methods to determine if the pricing kernel is the intertemporal marginal rate of substitution under recursive utility. We introduce a nonparametric Bayesian method that treats the pricing kernel as a latent variable and extracts it and its transition density from payoffs on 24 Fama-French portfolios, on bonds, and on payoffs that use conditioning information available when portfolios are formed. Our priors are formed from an examination of a Bansal-Yaron economy. Using both monthly data and annual data, we find that the data support recursive utility. Copyright , Oxford University Press.

Suggested Citation

  • Han Hong, 2007. "A Statistical Inquiry into the Plausibility of Recursive Utility," Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 523-559, Fall.
    • Handle: RePEc:oup:jfinec:v:5:y:2007:i:4:p:523-559
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbm013
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kakeu Johnson & Byron Sharri, 2016. "Optimistic about the future? How uncertainty and expectations about future consumption prospects affect optimal consumer behavior," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 171-192, January.
    2. Michael Creel & Jiti Gao & Han Hong & Dennis Kristensen, 2016. "Bayesian Indirect Inference and the ABC of GMM," Monash Econometrics and Business Statistics Working Papers 1/16, Monash University, Department of Econometrics and Business Statistics.
    3. Andrew Chia, 2021. "Automatically Differentiable Random Coefficient Logistic Demand Estimation," Papers 2106.04636, arXiv.org.
    4. Ron Gallant & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Generalized method of moments with latent variables," CeMMAP working papers 50/13, Institute for Fiscal Studies.
    5. Jiti Gao & Han Hong, 2014. "A Computational Implementation of GMM," Monash Econometrics and Business Statistics Working Papers 24/14, Monash University, Department of Econometrics and Business Statistics.
    6. A. Ronald Gallant & George Tauchen, 2021. "Cash Flows Discounted Using a Model-Free SDF Extracted under a Yield Curve Prior," JRFM, MDPI, vol. 14(3), pages 1-15, March.
    7. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
    8. Gallant, A. Ronald & Giacomini, Raffaella & Ragusa, Giuseppe, 2017. "Bayesian estimation of state space models using moment conditions," Journal of Econometrics, Elsevier, vol. 201(2), pages 198-211.
    9. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    10. Jonathan Willis & Russell Cooper, 2015. "Discounting: Investment Sensitivity and Aggregate Implications," 2015 Meeting Papers 607, Society for Economic Dynamics.
    11. A. Ronald Gallant, 2020. "Complementary Bayesian method of moments strategies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 422-439, June.

    More about this item

    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:oup:jfinec:v:5:y:2007:i:4:p:523-559. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.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.