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The age-period cohort problem: set identification and point identification

Author

Listed:
  • Martin Browning

    () (Institute for Fiscal Studies and University of Oxford)

  • Ian Crawford

    () (Institute for Fiscal Studies and University of Oxford)

  • Marike Knoef

    (Institute for Fiscal Studies)

Abstract

"Only entropy comes easily" - Anton Chekhov Various methods have been used to overcome the point identification problem inherent in the linear age-period-cohort model. This paper presents a set-identification result for the model and then considers the use of the maximum-entropy principle as a vehicle for achieving point identification. We present two substantive applications (US female mortality data and UK female labor force participation) and compare the results from our approach to some of the solutions in the literature.

Suggested Citation

  • Martin Browning & Ian Crawford & Marike Knoef, 2012. "The age-period cohort problem: set identification and point identification," CeMMAP working papers CWP02/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:02/12
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    File URL: http://cemmap.ifs.org.uk/wps/cwp021212.pdf
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    Cited by:

    1. Andreas Fagereng & Charles Gottlieb & Luigi Guiso, 2017. "Asset Market Participation and Portfolio Choice over the Life-Cycle," Journal of Finance, American Finance Association, vol. 72(2), pages 705-750, April.
    2. Claire Crawford & Lorraine Dearden & Ellen Greaves, 2013. "Identifying the drivers of month of birth differences in educational attainment," DoQSS Working Papers 13-07, Department of Quantitative Social Science - UCL Institute of Education, University College London.
    3. Kadija Charni & Stephen Bazen, 2017. "Do earnings really decline for older workers?," International Journal of Manpower, Emerald Group Publishing, vol. 38(1), pages 4-24, April.
    4. Rulof Burger & Servaas Berg & Dieter Fintel, 2015. "The Unintended Consequences of Education Policies on South African Participation and Unemployment," South African Journal of Economics, Economic Society of South Africa, vol. 83(1), pages 74-100, March.
    5. Rulof P Burger & Ronelle Burger & Laura Rossouw, 2012. "The fertility transition in South Africa: A retrospective panel data analysis," Development Southern Africa, Taylor & Francis Journals, vol. 29(5), pages 738-755, December.
    6. Andreas Tischbirek, 2016. "Long-Term Government Debt and Household Portfolio Composition," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 16.17, Université de Lausanne, Faculté des HEC, DEEP.
    7. Kadija Charni, 2016. "Is it Better to Work When We Are Older? An Empirical Comparison Between France and Great Britain," Working Papers halshs-01393268, HAL.
    8. Beatty, Timothy K.M. & Lin, Biing-Hwan & Smith, Travis A., 2013. "The Effects of Age and Birth Cohort on Dietary Quality in the United States," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 151426, Agricultural and Applied Economics Association.
    9. Victor Puac-Polanco & Katherine M. Keyes & Guohua Li, 2016. "Mortality from motorcycle crashes: the baby-boomer cohort effect," Injury Epidemiology, Springer;Columbia University Medical Center, vol. 3(1), pages 1-6, December.
    10. Andreas Fagereng & Elin Halvorsen, 2015. "Imputing consumption from Norwegian income and wealth registry data," Discussion Papers 831, Statistics Norway, Research Department.
    11. repec:eee:joecag:v:7:y:2016:i:c:p:64-68 is not listed on IDEAS
    12. Bonsang, Eric & Dohmen, Thomas, 2015. "Risk attitude and cognitive aging," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 112-126.

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