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A Smoothing Cohort Model in Age–Period–Cohort Analysis With Applications to Homicide Arrest Rates and Lung Cancer Mortality Rates

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  • Wenjiang J. Fu

    (Michigan State University, East Lansing, Australian National University, Canberra, fuw@msu.edu)

Abstract

This article considers the effects of age, period, and cohort in social studies and chronic disease epidemiology through age–period–cohort (APC) analysis. These factors are linearly dependent; thus, the multiple classification model, a regression model that takes these factors as covariates in APC analysis, suffers from an identifiability problem with multiple estimators. A data set of homicide arrest rates is used to illustrate the problem. A smoothing cohort model is proposed that allows flexible structure of the effects for age, period, and cohort and avoids the identifiability problem. Results are provided for the consistency of estimation of model intercept and age effects as the number of periods goes to infinity under a mild bounded cohort condition. This also leads to consistent estimation for period and cohort effects. Analyses of homicide arrest rate and lung cancer mortality rate data demonstrate that the smoothing cohort model yields unique parameter estimation with sensible trend interpretation.

Suggested Citation

  • Wenjiang J. Fu, 2008. "A Smoothing Cohort Model in Age–Period–Cohort Analysis With Applications to Homicide Arrest Rates and Lung Cancer Mortality Rates," Sociological Methods & Research, , vol. 36(3), pages 327-361, February.
    • Handle: RePEc:sae:somere:v:36:y:2008:i:3:p:327-361
    DOI: 10.1177/0049124107310637
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    References listed on IDEAS

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    1. Hong Cheng & Lesa Aylward & Colleen Beall & Thomas B. Starr & Robert C. Brunet & Gaétan Carrier & Elizabeth Delzell, 2006. "TCDD Exposure‐Response Analysis and Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 26(4), pages 1059-1071, August.
    2. Peter D. Sasieni & Joanna Adams, 2000. "Analysis of cervical cancer mortality and incidence data from England and Wales: evidence of a beneficial effect of screening," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(2), pages 191-209.
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    Cited by:

    1. Aistov, Andrey, 2018. "Age-earnings profiles of different generations," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 23-42.
    2. Qiang Fu & Kenneth C. Land & Vicki L. Lamb, 2016. "Violent Physical Bullying Victimization at School: Has There Been a Recent Increase in Exposure or Intensity? An Age-Period-Cohort Analysis in the United States, 1991 to 2012," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 9(2), pages 485-513, June.
    3. Whittaker, William & Birch, Stephen & MacKenzie, Adrian & Murphy, Gail Tomblin, 2016. "Cohort effects on the need for health care and implications for health care planning in Canada," Health Policy, Elsevier, vol. 120(1), pages 81-88.
    4. Koji Karato & Oleksandr Movshuk & Chihiro Shimizu, 2015. "Semiparametric Model of Hedonic Housing Prices in Japan," Asian Economic Journal, East Asian Economic Association, vol. 29(4), pages 325-345, December.
    5. Dregan, Alex & Armstrong, David, 2009. "Age, cohort and period effects in the prevalence of sleep disturbances among older people: The impact of economic downturn," Social Science & Medicine, Elsevier, vol. 69(10), pages 1432-1438, November.

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