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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

Abstract

A 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 this approach is both incorrect and unnecessary: incorrect, because it generally produces inconsistent estimators of the structural parameters, and unnecessary, because consistent estimators can be obtained under weaker assumptions. I derive an estimation method that avoids the problems of the standard approach. I illustrate the method?s benefits analytically in a simple model of consumption inequality and numerically by reestimating the classic life-cycle consumption model of Gourinchas and Parker (2002).

Suggested Citation

  • Sam Schulhofer-Wohl, 2017. "The Age-Time-Cohort Problem and the Identification of Structural Parameters in Life-Cycle Models," Working Paper Series WP-2017-18, Federal Reserve Bank of Chicago.
  • Handle: RePEc:fip:fedhwp:wp-2017-18
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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, October.
    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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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. The age-time-cohort problem and the identification of structural parameters in life-cylce models
      by Christian Zimmermann in NEP-DGE blog on 2013-07-22 08:53:16

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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. 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.
    3. 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.
    4. Zoë Fannon & B. Nielsen, 2018. "Age-period cohort models," Economics Papers 2018-W04, Economics Group, Nuffield College, University of Oxford.
    5. Hippolyte d'Albis & Ikpidi Badji, 2017. "Les inégalités de niveaux de vie entre les générations en France," PSE-Ecole d'économie de Paris (Postprint) halshs-01524882, HAL.
    6. 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.
    7. Bardazzi, Rossella & Pazienza, Maria Grazia, 2017. "Switch off the light, please! Energy use, aging population and consumption habits," Energy Economics, Elsevier, vol. 65(C), pages 161-171.
    8. Jesse Rothstein, 2020. "The Lost Generation? Labor Market Outcomes for Post Great Recession Entrants," NBER Working Papers 27516, National Bureau of Economic Research, Inc.
    9. Jelnov, Pavel & Weiss, Yoram, 2020. "Influence in Economics and Aging," IZA Discussion Papers 12887, Institute of Labor Economics (IZA).
    10. Frank T Denton & Byron G Spencer & Terry A Yip, 2019. "Age-Income Dynamics Over The Life Course: Cohort Transition Patterns In Relative Income Based On Canadian Tax Returns," Department of Economics Working Papers 2019-02, McMaster University.
    11. 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.
    12. Bardazzi, Rossella & Pazienza, Maria Grazia, 2018. "Ageing and private transport fuel expenditure: Do generations matter?," Energy Policy, Elsevier, vol. 117(C), pages 396-405.
    13. 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.
    14. Päivi Kankaanranta, 2019. "A Cohort-Analysis of Age-Wealth Profile in Finland," Discussion Papers 130, Aboa Centre for Economics.

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    More about this item

    Keywords

    Age-time-cohort identification problem; Life-cycle models;

    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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