IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v56y2020i1d10.1007_s10614-018-9874-x.html
   My bibliography  Save this article

Conditional Correlation Demand Systems

Author

Listed:
  • Apostolos Serletis

    (University of Calgary)

  • Libo Xu

    (University of San Francisco)

Abstract

We address the estimation of singular demand systems with heteroscedastic disturbances. As in Serletis and Isakin (Econ Rev 36:1111–1122, 2017) and Serletis and Xu (Empir Econ, 2019, forthcoming) we assume that the covariance matrix of the errors of the demand system is time-varying, and contribute to the literature by considering the constant conditional correlation and dynamic conditional correlation parameterizations of the variance model. We derive a number of important practical results and also provide an empirical application to support our methodology.

Suggested Citation

  • Apostolos Serletis & Libo Xu, 2020. "Conditional Correlation Demand Systems," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 77-86, June.
    • Handle: RePEc:kap:compec:v:56:y:2020:i:1:d:10.1007_s10614-018-9874-x
    DOI: 10.1007/s10614-018-9874-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-018-9874-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-018-9874-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. William Barnett & Jia Liu & Ryan Mattson & Jeff Noort, 2013. "The New CFS Divisia Monetary Aggregates: Design, Construction, and Data Sources," Open Economies Review, Springer, vol. 24(1), pages 101-124, February.
    2. BARTEN, Anton P., 1969. "Maximum likelihood estimation of a complete system of demand equations," LIDAM Reprints CORE 34, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Barten, A. P., 1969. "Maximum likelihood estimation of a complete system of demand equations," European Economic Review, Elsevier, vol. 1(1), pages 7-73.
    4. Blundell, Richard, 1988. "Consumer Behaviour: Theory and Empirical Evidence--a Survey," Economic Journal, Royal Economic Society, vol. 98(389), pages 16-65, March.
    5. Christensen, Laurits R & Jorgenson, Dale W & Lau, Lawrence J, 1975. "Transcendental Logarithmic Utility Functions," American Economic Review, American Economic Association, vol. 65(3), pages 367-383, June.
    6. Barnett, William A. & Serletis, Apostolos, 2008. "Consumer preferences and demand systems," Journal of Econometrics, Elsevier, vol. 147(2), pages 210-224, December.
    7. Apostolos Serletis & Libo Xu, 2020. "Demand systems with heteroscedastic disturbances," Empirical Economics, Springer, vol. 58(4), pages 1913-1921, April.
    8. Barnett, William A. & Serletis, Apostolos, 2008. "Measuring Consumer Preferences and Estimating Demand Systems," MPRA Paper 12318, University Library of Munich, Germany.
    9. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    10. Apostolos Serletis & Maksim Isakin, 2017. "Stochastic volatility demand systems," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1111-1122, November.
    11. Brown, Alan & Deaton, Angus S, 1972. "Surveys in Applied Economics: Models of Consumer Behaviour," Economic Journal, Royal Economic Society, vol. 82(328), pages 1145-1236, December.
    12. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    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. Apostolos Serletis & Libo Xu, 2020. "Demand systems with heteroscedastic disturbances," Empirical Economics, Springer, vol. 58(4), pages 1913-1921, April.
    2. Serletis, Apostolos & Xu, Libo, 2020. "Functional monetary aggregates, monetary policy, and business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    3. Serletis, Apostolos & Xu, Libo, 2022. "Interfuel substitution: A copula approach," Journal of Commodity Markets, Elsevier, vol. 28(C).
    4. Barnett, William A. & Serletis, Apostolos, 2008. "Consumer preferences and demand systems," Journal of Econometrics, Elsevier, vol. 147(2), pages 210-224, December.
    5. Nurul Hossain, A.K.M. & Serletis, Apostolos, 2017. "A century of interfuel substitution," Journal of Commodity Markets, Elsevier, vol. 8(C), pages 28-42.
    6. Serletis, Apostolos & Xu, Libo, 2019. "The demand for banking and shadow banking services," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 132-146.
    7. Apostolos Serletis & Maksim Isakin, 2017. "Stochastic volatility demand systems," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1111-1122, November.
    8. Keuzenkamp, Hugo A. & Barten, Anton P., 1995. "Rejection without falsification on the history of testing the homogeneity condition in the theory of consumer demand," Journal of Econometrics, Elsevier, vol. 67(1), pages 103-127, May.
    9. Clements, Kenneth W. & Gao, Grace, 2015. "The Rotterdam demand model half a century on," Economic Modelling, Elsevier, vol. 49(C), pages 91-103.
    10. Serletis, Apostolos & Xu, Libo, 2021. "The welfare cost of inflation," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    11. Barnett, William A. & Serletis, Apostolos, 2008. "Measuring Consumer Preferences and Estimating Demand Systems," MPRA Paper 12318, University Library of Munich, Germany.
    12. Serletis, Apostolos & Xu, Libo, 2023. "Consumer preferences, the demand for Divisia money, and the welfare costs of inflation," Journal of Macroeconomics, Elsevier, vol. 75(C).
    13. Serletis, Apostolos & Shahmoradi, Asghar, 2010. "Consumption effects of government purchases," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 892-905, September.
    14. Vik Singh, 2005. "Estimating a third-order translog demand system using Canadian micro-data," Econometrics 0512011, University Library of Munich, Germany.
    15. Barnett, William A. & Serletis, Apostolos, 2008. "The Differential Approach to Demand Analysis and the Rotterdam Model," MPRA Paper 12319, University Library of Munich, Germany.
    16. William A. Barnett & Isaac Kalonda Kanyama, 2013. "Time-varying parameters in the almost ideal demand system and the Rotterdam model: will the best specification please stand up?," Applied Economics, Taylor & Francis Journals, vol. 45(29), pages 4169-4183, October.
    17. Hossain, A. K. M. Nurul & Serletis, Apostolos, 2020. "Biofuel substitution in the U.S. transportation sector," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    18. Ignacio, Escañuela Romana, 2019. "The elasticities of passenger transport demand in the Northeast Corridor," Research in Transportation Economics, Elsevier, vol. 78(C).
    19. Adrian R. Fleissig, 2016. "Changing Trends in U.S. Alcohol Demand," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 44(3), pages 263-276, September.
    20. William A. Barnett & Isaac Kalonda Kanyama, 2013. "Time-varying parameters in the almost ideal demand system and the Rotterdam model: will the best specification please stand up?," Applied Economics, Taylor & Francis Journals, vol. 45(29), pages 4169-4183, October.

    More about this item

    Keywords

    Flexible functional forms; Demand systems; Volatility;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

    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:kap:compec:v:56:y:2020:i:1:d:10.1007_s10614-018-9874-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.