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, Society for Financial Econometrics, 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. 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.
    2. Ron Gallant & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Generalized method of moments with latent variables," CeMMAP working papers CWP50/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. 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.
    4. 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.
    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. repec:eee:econom:v:201:y:2017:i:2:p:198-211 is not listed on IDEAS

    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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/sofieea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.