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The Age-Time-Cohort Problem and the Identification of Structural Parameters in Life-Cycle Models

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  • Sam Schulhofer-Wohl

    (Federal Reserve Bank of Minneapolis)

Abstract

The standard approach to estimating structural parameters in life-cycle models imposes sufficient assumptions on the data to identify the "age profile" of outcomes, then chooses model parameters so that the model's age profile matches this empirical age profile. I show that the standard approach is both incorrect and unnecessary: incorrect, because it generically produces inconsistent estimators of the structural parameters, and unnecessary, because consistent estimators can be obtained under weaker fewer assumptions. I derive an identification method that avoids the problems of the standard approach and illustrate its benefits in a simple model of consumption inequality.

Suggested Citation

  • Sam Schulhofer-Wohl, 2012. "The Age-Time-Cohort Problem and the Identification of Structural Parameters in Life-Cycle Models," 2012 Meeting Papers 575, Society for Economic Dynamics.
    • Handle: RePEc:red:sed012:575
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    References listed on IDEAS

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    1. Mariacristina De Nardi & Eric French & John B. Jones, 2010. "Why Do the Elderly Save? The Role of Medical Expenses," Journal of Political Economy, University of Chicago Press, vol. 118(1), pages 39-75, February.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    3. Sam Schulhofer-Wohl & Yang Yang, 2011. "Modeling the evolution of age and cohort effects in social research," Staff Report 461, Federal Reserve Bank of Minneapolis.
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    Citations

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    Cited by:

    1. Luiz Mello & Simone Schotte & Erwin R. Tiongson & Hernan Winkler, 2017. "Greying the Budget: Ageing and Preferences over Public Policies," Kyklos, Wiley Blackwell, vol. 70(1), pages 70-96, February.
    2. Enrico Moretti & Daniel J. Wilson, 2017. "The Effect of State Taxes on the Geographical Location of Top Earners: Evidence from Star Scientists," American Economic Review, American Economic Association, vol. 107(7), pages 1858-1903, July.
    3. repec:nse:ecosta:ecostat_2017_491-492_7 is not listed on IDEAS
    4. Hippolyte d’Albis & Ikpidi Badji, 2017. "Intergenerational inequalities in standards of living in France," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Études Économiques (INSEE), issue 491-492, pages 71-92.
    5. Shutao Cao & Césaire Meh & José-Víctor Ríos-Rull & Yaz Terajima, 2018. "The Welfare Cost of Inflation Revisited: The Role of Financial Innovation and Household Heterogeneity," Staff Working Papers 18-40, Bank of Canada.
    6. G. C. Lim & Q. Zeng, 2016. "Consumption, Income, and Wealth: Evidence from Age, Cohort, and Period Elasticities," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(3), pages 489-508, September.
    7. Hippolyte D'Albis & Ikpidi Badji, 2017. "Les inégalités de niveaux de vie entre les générations en France," Post-Print halshs-01524882, HAL.
    8. repec:eee:enepol:v:117:y:2018:i:c:p:396-405 is not listed on IDEAS
    9. Lorenz Kueng & Mu-Jeung Yang & Bryan Hong, 2014. "Sources of Firm Life-Cycle Dynamics: Differentiating Size vs. Age Effects," NBER Working Papers 20621, National Bureau of Economic Research, Inc.
    10. repec:eee:eneeco:v:65:y:2017:i:c:p:161-171 is not listed on IDEAS

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • J1 - Labor and Demographic Economics - - Demographic Economics

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