IDEAS home Printed from https://ideas.repec.org/p/cop/wpaper/g-120.html
   My bibliography  Save this paper

Compositional Data Analysis and Zeros in Micro Data

Author

Listed:
  • Jane M. Fry
  • Tim R.L. Fry
  • Keith R. McLaren

Abstract

The application of compositional data analysis methods in economics has some attraction. In particular, this methodology ensures that the stochastic component of budget share models will satisfy the restriction of shares to the unit simplex. The methodology relies upon the use of log-ratios in the statistical analysis. Such an approach is not possible when the data to be analyzed includes observations where the observed budget share is zero. We therefore extend the methods of compositional data analysis to the situation where the data to be analyzed includes observations where the observed budget share is zero. The modified compositional data methods are discussed both in statistical terms and through potential economic interpretations of the method. Further, the modified methodology is applied to the 1988 Australian Household Expenditure Survey yielding estimates for a system of Engel curves.

Suggested Citation

  • Jane M. Fry & Tim R.L. Fry & Keith R. McLaren, 1996. "Compositional Data Analysis and Zeros in Micro Data," Centre of Policy Studies/IMPACT Centre Working Papers g-120, Victoria University, Centre of Policy Studies/IMPACT Centre.
  • Handle: RePEc:cop:wpaper:g-120
    as

    Download full text from publisher

    File URL: http://www.copsmodels.com/ftp/workpapr/g-120.pdf
    File Function: Initial version, 1996-03
    Download Restriction: no

    File URL: http://www.copsmodels.com/elecpapr/g-120.htm
    File Function: Local abstract: may link to additional material.
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Maureen T. Rimmer & Alan A. Powell, 1994. "Engel Flexibility in Household Budget Studies: Non-parametric Evidence versus Standard Functional Forms," Centre of Policy Studies/IMPACT Centre Working Papers op-79, Victoria University, Centre of Policy Studies/IMPACT Centre.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hikaru Hasegawa & Kazuhiro Ueda & Kunie Mori, 2008. "Estimation of Engel Curves from Survey Data with Zero Expenditures," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 535-558, August.
    2. Tsagris, Michail, 2014. "The k-NN algorithm for compositional data: a revised approach with and without zero values present," MPRA Paper 65866, University Library of Munich, Germany.
    3. ARATA Yoshiyuki & ONOZAKI Tamotsu, 2017. "A Compositional Data Analysis of Market Share Dynamics," Discussion papers 17076, Research Institute of Economy, Trade and Industry (RIETI).
    4. Johan Fourie & Dieter von Fintel, 2010. "The dynamics of inequality in a newly settled, pre-industrial society: the case of the Cape Colony," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 4(3), pages 229-267, October.
    5. Andreas Chai & Christian Kiedaisch & Nicholas Rohde, 2017. "The saturation of spending diversity and the truth about Mr Brown and Mrs Jones," Discussion Papers in Economics economics:201701, Griffith University, Department of Accounting, Finance and Economics.
    6. Andriansyah, Andriansyah & Messinis, George, 2016. "Intended use of IPO proceeds and firm performance: A quantile regression approach," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 14-30.
    7. Jack Gregory & David I. Stern, 2012. "Fuel Choices in Rural Maharashtra," CCEP Working Papers 1207, Centre for Climate Economics & Policy, Crawford School of Public Policy, The Australian National University.
    8. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    9. J. Arauzo & M. Manjón & M. Martín & A. Segarra, 2007. "Regional and Sector-specific Determinants of Industry Dynamics and the Displacement–replacement Effects," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 34(2), pages 89-115, April.
    10. Adam Butler & Chris Glasbey, 2008. "A latent Gaussian model for compositional data with zeros," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 505-520.
    11. Wang, Huiwen & Liu, Qiang & Mok, Henry M.K. & Fu, Linghui & Tse, Wai Man, 2007. "A hyperspherical transformation forecasting model for compositional data," European Journal of Operational Research, Elsevier, vol. 179(2), pages 459-468, June.
    12. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2016. "Nonparametric hypothesis testing for equality of means on the simplex," MPRA Paper 72771, University Library of Munich, Germany.
    13. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2016. "Improved classi cation for compositional data using the $\alpha$-transformation," MPRA Paper 67657, University Library of Munich, Germany.
    14. Michail Tsagris & Simon Preston & Andrew T. A. Wood, 2016. "Improved Classification for Compositional Data Using the α-transformation," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 243-261, July.
    15. Terence Mills, 2010. "Forecasting compositional time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(4), pages 673-690, June.
    16. Gordon Anderson & Maria Grazia Pittau & Roberto Zelli, 2016. "Assessing the convergence and mobility of nations without artificially specified class boundaries," Journal of Economic Growth, Springer, vol. 21(3), pages 283-304, September.

    More about this item

    Keywords

    Engel Curves; Modified Almost Ideal Demand System; Composi- tional Data Analysis; Australian H E S data;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    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:cop:wpaper:g-120. 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: (Mark Horridge). General contact details of provider: http://edirc.repec.org/data/cpmonau.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.

    If CitEc recognized a 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.

    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.