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A Statistical Inquiry into the Plausibility of Recursive Utility

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  • 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
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbm013
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    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. Giacomini, Raffaella & Ragusa, Giuseppe & Gallant, A. Ronald, 2013. "Generalized Method of Moments with Latent Variables," CEPR Discussion Papers 9692, C.E.P.R. Discussion Papers.
    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.

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