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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).

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  • Sam Schulhofer‐Wohl, 2018. "The age‐time‐cohort problem and the identification of structural parameters in life‐cycle models," Quantitative Economics, Econometric Society, vol. 9(2), pages 643-658, July.
  • Handle: RePEc:wly:quante:v:9:y:2018:i:2:p:643-658
    DOI: 10.3982/QE738
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    1. Jonathan Heathcote & Kjetil Storesletten & Giovanni L. Violante, 2005. "Two Views of Inequality Over the Life Cycle," Journal of the European Economic Association, MIT Press, vol. 3(2-3), pages 765-775, 04/05.
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    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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    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. Cao, Shutao & Meh, Césaire A. & Ríos-Rull, José-Víctor & Terajima, Yaz, 2021. "The welfare cost of inflation revisited: The role of financial innovation and household heterogeneity," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 366-380.
    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.
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    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

    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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