IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-11-00007.html
   My bibliography  Save this article

Estimation of consumption-capital asset pricing model (C-CAPM) with two clusters of consumption expenditures

Author

Listed:
  • Atsushi Maki

    (Department of Economics, Tokyo International University)

  • Kenji Wada

    (Faculty of Business and Commerce, Keio University)

Abstract

In this paper, we develop a new model that explicitly considers two endogenous consumption items and investigates its applicability to consumption-capital asset pricing model (C-CAPM) by testing it with various sets of instruments. We found that our model is not rejected with reasonable values for both risk aversion and time preference parameters.

Suggested Citation

  • Atsushi Maki & Kenji Wada, 2011. "Estimation of consumption-capital asset pricing model (C-CAPM) with two clusters of consumption expenditures," Economics Bulletin, AccessEcon, vol. 31(2), pages 1183-1187.
  • Handle: RePEc:ebl:ecbull:eb-11-00007
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I2-P110.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghysels,Eric & Osborn,Denise R., 2001. "The Econometric Analysis of Seasonal Time Series," Cambridge Books, Cambridge University Press, number 9780521565882.
    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. Christoffersen, Peter & Ghysels, Eric & Swanson, Norman R., 2002. "Let's get "real" about using economic data," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 343-360, August.
    2. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    3. Franses, Philip Hans, 2013. "Data revisions and periodic properties of macroeconomic data," Economics Letters, Elsevier, vol. 120(2), pages 139-141.
    4. Chambers, Marcus J. & Ercolani, Joanne S. & Taylor, A.M. Robert, 2014. "Testing for seasonal unit roots by frequency domain regression," Journal of Econometrics, Elsevier, vol. 178(P2), pages 243-258.
    5. Roberto Cellini & Tiziana Cuccia, 2013. "Museum and monument attendance and tourism flow: a time series analysis approach," Applied Economics, Taylor & Francis Journals, vol. 45(24), pages 3473-3482, August.
    6. Jacek Kotlowski, 2005. "Money and prices in the Polish economy. Seasonal cointegration approach," Working Papers 20, Department of Applied Econometrics, Warsaw School of Economics.
    7. del Barrio Castro, Tomás & Hecq, Alain, 2016. "Testing for deterministic seasonality in mixed-frequency VARs," Economics Letters, Elsevier, vol. 149(C), pages 20-24.
    8. Curry, Bruce, 2007. "Neural networks and seasonality: Some technical considerations," European Journal of Operational Research, Elsevier, vol. 179(1), pages 267-274, May.
    9. Altansukh, Gantungalag & Becker, Ralf & Bratsiotis, George J. & Osborn, Denise R., 2017. "What is the Globalisation of Inflation?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 74, pages 1-27.
    10. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    11. Constantin ANGHELACHE & Alexandru MANOLE & Mădălina Gabriela ANGHEL, 2016. "The major economic evolution of Romania by the middle of 2016," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(609), W), pages 165-182, Winter.
    12. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    13. del Barrio Castro, Tomás & Osborn, Denise R., 2023. "Periodic Integration and Seasonal Unit Roots," MPRA Paper 117935, University Library of Munich, Germany, revised 2023.
    14. Politis, Dimitris, 2016. "HEGY test under seasonal heterogeneity," University of California at San Diego, Economics Working Paper Series qt2q4054kf, Department of Economics, UC San Diego.
    15. Nahata, Babu & Izyumov, Alexei & Busygin, Vladimir & Mishura, Anna, 2007. "Application of Ramsey model in transition economy: A Russian case study," Energy Economics, Elsevier, vol. 29(1), pages 105-125, January.
    16. Helmut Lütkepohl, 2013. "Vector autoregressive models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 6, pages 139-164, Edward Elgar Publishing.
    17. Otero, Jesus & Smith, Jeremy & Giulietti, Monica, 2005. "Testing for seasonal unit roots in heterogeneous panels," Economics Letters, Elsevier, vol. 86(2), pages 229-235, February.
    18. Vaz, Lucélia Viviane & Filho, Getulio Borges da Silveira, 2017. "Functional Autoregressive Models: An Application to Brazilian Hourly Electricity Load," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(2), November.
    19. Haldrup, Niels & Hylleberg, Svend & Pons, Gabriel & Sanso, Andreu, 2007. "Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 21-32, January.
    20. Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.

    More about this item

    Keywords

    C-CAPM; multiple consumption commodities; inter-temporal and intra-temporal choice;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:ebl:ecbull:eb-11-00007. 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: John P. Conley (email available below). General contact details of provider: .

    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.