IDEAS home Printed from https://ideas.repec.org/h/elg/eechap/14533_6.html
   My bibliography  Save this book chapter

Uncertainty and Projections of the Demand for Mail

In: Multi-Modal Competition and the Future of Mail

Author

Listed:
  • Frédérique Fève
  • Jean-Pierre Florens
  • Leticia Veruete-McKay
  • Frank Rodriguez
  • Soterios Steri
  • Frank Rodriguez

Abstract

This compilation of original papers selected from the 19th Conference on Postal and Delivery Economics and authored by an international cast of economists, lawyers, regulators and industry practitioners addresses perhaps the most significant problem that has ever faced the postal sector – electronic competition from information and communication technologies. This has increased significantly over the last few years with a consequent serious drop in mail volume.

Suggested Citation

  • Frédérique Fève & Jean-Pierre Florens & Leticia Veruete-McKay & Frank Rodriguez & Soterios Steri & Frank Rodriguez, 2012. "Uncertainty and Projections of the Demand for Mail," Chapters, in: Michael A. Crew & Paul R. Kleindorfer (ed.), Multi-Modal Competition and the Future of Mail, chapter 6, Edward Elgar Publishing.
  • Handle: RePEc:elg:eechap:14533_6
    as

    Download full text from publisher

    File URL: https://www.elgaronline.com/view/9780857935816.00011.xml
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Veruete-McKay Leticia & Soteri Soterios & Nankervis John C. & Rodriguez Frank, 2011. "Letter Traffic Demand in the UK: An Analysis by Product and Envelope Content Type," Review of Network Economics, De Gruyter, vol. 10(3), pages 1-28, September.
    2. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
    3. Jean-Pierre Florens & Vêlayoudom Marimoutou & Anne Peguin-Feissolle, 2007. "Econometric Modeling and Inference," Post-Print halshs-00390164, HAL.
    4. Florens,Jean-Pierre & Marimoutou,Velayoudom & Peguin-Feissolle,Anne, 2007. "Econometric Modeling and Inference," Cambridge Books, Cambridge University Press, number 9780521700061, September.
    5. Florens,Jean-Pierre & Marimoutou,Velayoudom & Peguin-Feissolle,Anne, 2007. "Econometric Modeling and Inference," Cambridge Books, Cambridge University Press, number 9780521876407, April.
    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. Xiaohong Chen & Andres Santos, 2018. "Overidentification in Regular Models," Econometrica, Econometric Society, vol. 86(5), pages 1771-1817, September.
    2. Julio Vicente Cateia, 2019. "Guinea-Bissau Trade: A Panel Data Analysis," Asian Development Policy Review, Asian Economic and Social Society, vol. 7(4), pages 277-296, December.
    3. Atangana Ondoa, Henri & Tomo, Christian Parfait, 2022. "Déterminants des ménages et accès au crédit dans les tontines au Cameroun [Determinants of households and access to credit in Cameroon]," MPRA Paper 113629, University Library of Munich, Germany, revised Jun 2022.
    4. An, Lihua & Nkurunziza, Sévérien & Fung, Karen Y. & Krewski, Daniel & Luginaah, Isaac, 2009. "Shrinkage estimation in general linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2537-2549, May.
    5. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    6. Lindh, Thomas & Malmberg, Bo, 2007. "Demographically based global income forecasts up to the year 2050," International Journal of Forecasting, Elsevier, vol. 23(4), pages 553-567.
    7. Flouris, Triant & Walker, Thomas, 2005. "Financial Comparisons Across Different Business Models in the Canadian Airline Industry," 46th Annual Transportation Research Forum, Washington, D.C., March 6-8, 2005 208157, Transportation Research Forum.
    8. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    9. Kenneth Gillingham & William D. Nordhaus & David Anthoff & Geoffrey Blanford & Valentina Bosetti & Peter Christensen & Haewon McJeon & John Reilly & Paul Sztorc, 2015. "Modeling Uncertainty in Climate Change: A Multi-Model Comparison," NBER Working Papers 21637, National Bureau of Economic Research, Inc.
    10. Bhattacharya, Prasad S. & Thomakos, Dimitrios D., 2008. "Forecasting industry-level CPI and PPI inflation: Does exchange rate pass-through matter?," International Journal of Forecasting, Elsevier, vol. 24(1), pages 134-150.
    11. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
    12. Seitz, Franz & Baumann, Ursel & Albuquerque, Bruno, 2015. "The information content of money and credit for US activity," Working Paper Series 1803, European Central Bank.
    13. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    14. Brüggemann, Ralf & Lütkepohl, Helmut, 2013. "Forecasting contemporaneous aggregates with stochastic aggregation weights," International Journal of Forecasting, Elsevier, vol. 29(1), pages 60-68.
    15. Wolfgang Polasek, 2013. "Forecast Evaluations for Multiple Time Series: A Generalized Theil Decomposition," Working Paper series 23_13, Rimini Centre for Economic Analysis.
    16. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    17. Costantini, Mauro & Pappalardo, Carmine, 2010. "A hierarchical procedure for the combination of forecasts," International Journal of Forecasting, Elsevier, vol. 26(4), pages 725-743, October.
    18. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    19. Dick Dijk & Siem Jan Koopman & Michel Wel & Jonathan H. Wright, 2014. "Forecasting interest rates with shifting endpoints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 693-712, August.
    20. repec:onb:oenbwp:y::i:73:b:1 is not listed on IDEAS
    21. Koo, Bonsoo & Seo, Myung Hwan, 2015. "Structural-break models under mis-specification: Implications for forecasting," Journal of Econometrics, Elsevier, vol. 188(1), pages 166-181.

    More about this item

    Keywords

    Economics and Finance;

    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:elg:eechap:14533_6. 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: Darrel McCalla (email available below). General contact details of provider: http://www.e-elgar.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.