IDEAS home Printed from https://ideas.repec.org/a/bla/buecrs/v57y2005i4p347-367.html
   My bibliography  Save this article

Averaging Income Distributions

Author

Listed:
  • William E. Griffiths
  • Duangkamon Chotikapanich
  • D. S. Prasada Rao

Abstract

Various inequality and social welfare measures often depend heavily on the choice of a distribution of income. Picking a distribution that best fits the data involves throwing away information and does not allow for the fact that a wrong choice can be made. Instead, Bayesian model averaging utilizes a weighted average of the results from a number of income distributions, with each weight given by the probability that a distribution is ‘correct’. In this study, prior densities are placed on mean income, the mode of income and the Gini coefficient for Australian income units with one parent (1997–8). Then, using grouped sample data on incomes, posterior densities for the mean and mode of income and the Gini coefficient are derived for a variety of income distributions. The model‐averaged results from these income distributions are obtained.

Suggested Citation

  • William E. Griffiths & Duangkamon Chotikapanich & D. S. Prasada Rao, 2005. "Averaging Income Distributions," Bulletin of Economic Research, Wiley Blackwell, vol. 57(4), pages 347-367, October.
    • Handle: RePEc:bla:buecrs:v:57:y:2005:i:4:p:347-367
    DOI: 10.1111/j.0307-3378.2005.00226.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.0307-3378.2005.00226.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.0307-3378.2005.00226.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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    2. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
    3. Gary Koop & Simon M. Potter, 2003. "Forecasting in large macroeconomic panels using Bayesian Model Averaging," Staff Reports 163, Federal Reserve Bank of New York.
    4. Branko Milanovic, 2002. "True World Income Distribution, 1988 and 1993: First Calculation Based on Household Surveys Alone," Economic Journal, Royal Economic Society, vol. 112(476), pages 51-92, January.
    5. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    6. Griffiths, William E & Chotikapanich, Duangkamon, 1997. "Bayesian Methodology for Imposing Inequality Constraints on a Linear Expenditure System with Demographic Factors," Australian Economic Papers, Wiley Blackwell, vol. 36(69), pages 321-341, December.
    7. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    8. Elliott, Graham & Timmermann, Allan, 2004. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, Elsevier, vol. 122(1), pages 47-79, September.
    9. Yang, Yuhong, 2004. "Combining Forecasting Procedures: Some Theoretical Results," Econometric Theory, Cambridge University Press, vol. 20(1), pages 176-222, February.
    10. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    11. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    12. Hasegawa, Hikaru & Kozumi, Hideo, 2003. "Estimation of Lorenz curves: a Bayesian nonparametric approach," Journal of Econometrics, Elsevier, vol. 115(2), pages 277-291, August.
    13. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    14. Danilov, D.L. & Magnus, J.R., 2001. "On the Harm that Pretesting Does," Discussion Paper 2001-37, Tilburg University, Center for Economic Research.
    15. Ray Barrell & Amanda Choy, 2003. "Economic Integration and Openness in Europe and East Asia," National Institute of Economic and Social Research (NIESR) Discussion Papers 214, National Institute of Economic and Social Research.
    16. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    17. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
    18. Sen, Amartya K, 1976. "Poverty: An Ordinal Approach to Measurement," Econometrica, Econometric Society, vol. 44(2), pages 219-231, March.
    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. Michel Lubrano & Zhou Xun, 2021. "The Bayesian approach to poverty measurement," Working Papers halshs-03234072, HAL.
    2. Hikaru Hasegawa & Kazuhiro Ueda, 2016. "Multidimensional inequality for current status of Japanese private companies’ employees," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 357-373, December.
    3. Duangkamon Chotikapanich & William E. Griffiths, 2006. "Bayesian Assessment of Lorenz and Stochastic Dominance in Income Distributions," Department of Economics - Working Papers Series 960, The University of Melbourne.
    4. Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print halshs-04135764, HAL.
    5. Omar Abdul Rahman Kittaneh, 2019. "Estimating the Income Distribution of Some Muslim Countries Based on Entropy Measures تقدير توزيع الدخل لبعض الدول الإسلامية بناء على مقاييس عشوائية," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 32(1), pages 159-169, January.
    6. Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print hal-04347292, HAL.

    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. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
    2. Cheng, Gang & Yang, Yuhong, 2015. "Forecast combination with outlier protection," International Journal of Forecasting, Elsevier, vol. 31(2), pages 223-237.
    3. Duangkamon Chotikapanich & William Griffiths & Wasana Karunarathne & D.S. Prasada Rao, 2013. "Calculating Poverty Measures from the Generalised Beta Income Distribution," The Economic Record, The Economic Society of Australia, vol. 89, pages 48-66, June.
    4. Jakub Nowotarski, 2013. "Short-term forecasting of electricity spot prices using model averaging (Krótkoterminowe prognozowanie spotowych cen energii elektrycznej z wykorzystaniem uśredniania modeli)," HSC Research Reports HSC/13/17, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Eliana González, 2010. "Bayesian Model Averaging. An Application to Forecast Inflation in Colombia," Borradores de Economia 7013, Banco de la Republica.
    6. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
    7. Gholamreza Hajargsht & William E. Griffiths & Joseph Brice & D.S. Prasada Rao & Duangkamon Chotikapanich, 2011. "GMM Estimation of Income Distributions from Grouped Data," Department of Economics - Working Papers Series 1129, The University of Melbourne.
    8. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    9. Jos'e Miguel Flores-Contr'o, 2024. "The Gerber-Shiu Expected Discounted Penalty Function: An Application to Poverty Trapping," Papers 2402.11715, arXiv.org.
    10. Alonso Fernández, Andrés Modesto & Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Chu‐An Liu & Biing‐Shen Kuo, 2016. "Model averaging in predictive regressions," Econometrics Journal, Royal Economic Society, vol. 19(2), pages 203-231, June.
    12. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    13. Chen Zhuo & Yang Yuhong, 2007. "Time Series Models for Forecasting: Testing or Combining?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(1), pages 56-90, March.
    14. Carlo Altavilla & Matteo Ciccarelli, 2006. "Inflation Forecasts, Monetary Policy and Unemployment Dynamics: Evidence from the US and the Euro Area," Discussion Papers 7_2006, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy.
    15. Jana Eklund & Sune Karlsson, 2007. "Forecast Combination and Model Averaging Using Predictive Measures," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 329-363.
    16. Gang Cheng & Sicong Wang & Yuhong Yang, 2015. "Forecast Combination under Heavy-Tailed Errors," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    17. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    18. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    19. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    20. Gary Koop & Rodney Strachan & Herman van Dijk & Mattias Villani, 2004. "Bayesian Approaches to Cointegration," Discussion Papers in Economics 04/27, Division of Economics, School of Business, University of Leicester.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

    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:bla:buecrs:v:57:y:2005:i:4:p:347-367. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0307-3378 .

    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.