IDEAS home Printed from https://ideas.repec.org/a/bbz/fcpbbr/v3y2006i1p1-14.html

Estimating and Testing Two Consumption-Based Asset Pricing Models for Brazil: An Information-Theoretic Approach

Author

Listed:
  • Eurilton Araujo

    (IBMEC - SP)

Abstract

Since Brazilian data sets for consumption and asset returns are short and the standard GMM-based overidentifying restrictions test has low power in small samples, a GMM approach imposes difficulties to the evaluation of asset pricing kernels better suited to describe asset pricing phenomena in Brazil. This paper addresses the question of estimating and testing two asset pricing models, using an information-theoretic method of moments estimator, which minimizes the Kullback-Leibler Information Criterion (KLIC). The goal is to compare the traditional GMM method with the alternative information-theoretic approach, which has promising finite sample properties, focusing on over identifying restrictions test and parameter estimate.

Suggested Citation

  • Eurilton Araujo, 2006. "Estimating and Testing Two Consumption-Based Asset Pricing Models for Brazil: An Information-Theoretic Approach," Brazilian Business Review, Fucape Business School, vol. 3(1), pages 1-14, January.
    • Handle: RePEc:bbz:fcpbbr:v:3:y:2006:i:1:p:1-14
    as

    Download full text from publisher

    File URL: http://bbronline.com.br/index.php/bbr/article/download/455/686
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicolas A. Cuche & Martin K. Hess, 1999. "Estimating Monthly GDP In A General Kalman Filter Framework: Evidence From Switzerland," Working Papers 99.02, Swiss National Bank, Study Center Gerzensee.
    2. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
    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. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    2. Chang, Jinyuan & Chen, Song Xi & Chen, Xiaohong, 2015. "High dimensional generalized empirical likelihood for moment restrictions with dependent data," Journal of Econometrics, Elsevier, vol. 185(1), pages 283-304.
    3. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    4. Whitney K. Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Joachim Inkmann, 2000. "Finite Sample Properties of One-Step, Two-Step and Bootstrap Empirical Likelihood Approaches to Efficient GMM Estimation," Econometric Society World Congress 2000 Contributed Papers 0332, Econometric Society.
    6. Otsu, Taisuke, 2010. "On Bahadur efficiency of empirical likelihood," Journal of Econometrics, Elsevier, vol. 157(2), pages 248-256, August.
    7. repec:cep:stiecm:/2014/572 is not listed on IDEAS
    8. Hausman, Jerry & Lewis, Randall & Menzel, Konrad & Newey, Whitney, 2011. "Properties of the CUE estimator and a modification with moments," Journal of Econometrics, Elsevier, vol. 165(1), pages 45-57.
    9. Shane M. Sherlund, 2004. "Quasi Empirical Likelihood Estimation of Moment Condition Models," Econometric Society 2004 North American Summer Meetings 507, Econometric Society.
    10. Zhang, Jia & Shi, Haoming & Tian, Lemeng & Xiao, Fengjun, 2019. "Penalized generalized empirical likelihood in high-dimensional weakly dependent data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 270-283.
    11. In-Koo Cho & Kenneth Kasa, 2015. "Learning and Model Validation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(1), pages 45-82.
    12. Tanaka, Ken'ichiro & Toda, Alexis Akira, 2015. "Discretizing Distributions with Exact Moments: Error Estimate and Convergence Analysis," University of California at San Diego, Economics Working Paper Series qt7g23r5kh, Department of Economics, UC San Diego.
    13. de Figueiredo, Erik Alencar & Ziegelmann, Flávio Augusto, 2010. "Estimating income mobility using census data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4897-4903.
    14. Pierre Chausse, 2017. "Regularized Empirical Likelihood as a Solution to the No Moment," Working Papers 1708, University of Waterloo, Department of Economics, revised Nov 2017.
    15. Martins, Luis F. & Gabriel, Vasco J., 2009. "New Keynesian Phillips Curves and potential identification failures: A Generalized Empirical Likelihood analysis," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 561-571, December.
    16. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers 23/18, Institute for Fiscal Studies.
    17. Yuichi Kitamura & Taisuke Otsu & Kirill Evdokimov, 2013. "Robustness, Infinitesimal Neighborhoods, and Moment Restrictions," Econometrica, Econometric Society, vol. 81(3), pages 1185-1201, May.
    18. Li, Haiqi & Fan, Rui & Park, Sung Y., 2018. "Generalized empirical likelihood specification test robust to local misspecification," Economics Letters, Elsevier, vol. 171(C), pages 149-153.
    19. Martyn Andrews & Obbey Elamin & Alastair R. Hall & Kostas Kyriakoulis & Matthew Sutton, 2014. "Inference in the Presence of Redundant Moment Conditions and the Impact of Government Health Expenditure on Health Outcomes in England," Economics Discussion Paper Series 1401, Economics, The University of Manchester.
    20. Lô, Serigne N. & Ronchetti, Elvezio, 2012. "Robust small sample accurate inference in moment condition models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3182-3197.
    21. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.

    More about this item

    Keywords

    ;
    ;
    ;

    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:bbz:fcpbbr:v:3:y:2006:i:1:p:1-14. 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: Sarah Lasso The email address of this maintainer does not seem to be valid anymore. Please ask Sarah Lasso to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/fucapbr.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.