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28.08.2019

Bounding Analyses of Age-Period-Cohort Effects

verfasst von: Ethan Fosse, Christopher Winship

Erschienen in: Demography | Ausgabe 5/2019

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Abstract

For more than a century, researchers from a wide range of disciplines have sought to estimate the unique contributions of age, period, and cohort (APC) effects on a variety of outcomes. A key obstacle to these efforts is the linear dependence among the three time scales. Various methods have been proposed to address this issue, but they have suffered from either ad hoc assumptions or extreme sensitivity to small differences in model specification. After briefly reviewing past work, we outline a new approach for identifying temporal effects in population-level data. Fundamental to our framework is the recognition that it is only the slopes of an APC model that are unidentified, not the nonlinearities or particular combinations of the linear effects. One can thus use constraints implied by the data along with explicit theoretical claims to bound one or more of the APC effects. Bounds on these parameters may be nearly as informative as point estimates, even with relatively weak assumptions. To demonstrate the usefulness of our approach, we examine temporal effects in prostate cancer incidence and homicide rates. We conclude with a discussion of guidelines for further research on APC effects.

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Fußnoten
1
Following the convention in the APC literature, we use the shorthand of “effects” when referring to age, period, and cohort processes (e.g., Fienberg and Mason 1979; Glenn 1981; Mason et al. 1973; O’Brien 2015a; Yang and Land 2013a:). We discuss the issue of interpreting the coefficients from an APC model in the online appendix.
 
2
For example, researchers have examined verbal ability (Alwin 1991; Hauser and Huang 1997; Wilson and Gove 1999; Yang and Land 2006), social trust (Clark and Eisenstein 2013; Putnam 1995; Robinson and Jackson 2001; Schwadel and Stout 2012), party identification (Bartels and Jackman 2014; Ghitza and Gelman 2014; Hout and Knoke 1975; Tilley and Evans 2014), religious affiliation (Chaves 1989; Firebaugh and Harley 1991), drug use (Chen et al. 2003; Kerr et al. 2004; O’Malley et al. 1984; Vedøy 2014), obesity (Diouf et al. 2010; Fu and Land 2015; Reither et al. 2009), cancer (Clayton and Schifflers 1987; Liu et al. 2001), and mental health (Lavori et al. 1987; Lewinsohn et al. 1993; Yang 2008).
 
3
Researchers have used various terms in the literature to refer to the linear and nonlinear effects of an APC model. In this article, we refer to the linear effects as “slopes” or “linear effects”; conversely, we refer to the nonlinear effects as “nonlinearities” or “deviations.” By “effects” or “overall effects,” we refer to the combination of the linear and nonlinear effects.
 
4
However, APC analysis arguably dates back to at least the 1860s, predating the eponymous diagrams of Wilhelm Lexis (see Keiding 2011).
 
5
As Rodgers (1982a:785) cautioned, “Although a constraint of the type described by Mason et al. (1973; 1979) seems trivial, in fact it is exquisitely precise and has effects that are multiplied so that even a slight inconsistency between the constraint and reality, or small measurement errors, can have very large effects on estimates.” However, see also the reply by Smith et al. (1982) as well as the rejoinder by Rodgers (1982b).
 
6
For notable examples of researchers identifying APC effects using explicit, theoretically motivated equality constraints, see Mason and Smith (1985) and O’Brien (2015a).
 
7
For alternative perspectives on these techniques, see Fu (2016) and Land et al. (2016).
 
8
For example, with reference to the IE, Yang and Land (2013a:119) noted that “the objective of the IE is not to estimate the unidentifiable regression coefficient vector.” That is, the IE finds the point on the solution line closest to the origin in terms of Euclidean distance, but it does not necessarily recover the actual age, period, and cohort effects.
 
9
For simplicity of exposition we assume that age and period are aggregated into intervals of equal width. Additional complications arise when the age and period intervals are not equally spaced, because this can generate artifactual cyclical patterns. For approaches to estimating temporal effects when age and period intervals are unequal, see Holford (2006).
 
10
Alternatively, one could fix the parameters at one of the levels to 0. By convention, researchers typically fix to 0 the first set of levels (e.g., αi = 1 = πj = 1 = γk = 1 = 0) or the last set (e.g., αi = I= πj = J = γk = K = 0), although other sets could be used.
 
11
They are equivalent in the sense that as basis vectors they span the same space.
 
12
The null vector is unique up to multiplication by a scalar.
 
13
A simple linear transformation can be used to convert agei to ii* because ii* = (ageiage*) / (Δage), where age* is the midpoint for all age groups, and ∆age is the fixed difference between the midpoints. For example, suppose we have age1 = 32, age2 = 37, age3 = 42, age4 = 47, and age5 = 52. The midpoint across all age groups is 42, and the fixed difference between the groups is 5. Thus, we can calculate that age1 = 32 equals (32 – 42) / 5 = –2, which is equivalent to ii* = 1 – 3 = –2.
 
14
There is considerable disagreement in the social science and statistics literature on the causal status of nonmanipulable variables. As Rubin (1986) and Holland (1986) argued, such variables do not themselves have well-defined causal effects. However, in Pearl’s (2009) framework, these variables may be ascribed a causal status, with corresponding counterfactuals, even though they are not manipulable.
 
15
One could also estimate the θs using Yijk = μ + (periodj)(θ1) + (cohortk)(θ2 – θ1) + εijk or Yijk = μ + (agei)(θ1 – θ2) + (periodj)(θ2) + εijk.
 
16
The estimates when the age slope is constrained to equal 0 are α* = 0, π* = θ1, and γ* = θ2 – θ1, and the corresponding estimates when the cohort slope is fixed to 0 are α* = θ1 – θ2, π* = θ2, and γ* = 0.
 
17
Figure 1 is a graphical representation of the APC identification problem. If there were no linear dependency, we would have three planes intersecting at a single point in the parameter space (O’Brien 2015a).
 
18
The age-period-cohort origin is (0, 0, 0), but this is not directly visible on the 2D-APC graph unless θ2 − θ1 = 0.
 
19
The –1 slope relating period to age and cohort as well as the differing direction of the period axis relative to the age and cohort axes in the 2D-APC graph are reflected in the opposing sign of ν in Eq. (4) for the period slope compared with that for the age and cohort slopes.
 
20
Likewise, each plane in Fig. 1 can be thought of as a function of linear equations based on θ1 and θ2. For example, the age-period plane is defined by θ1 = α + π, so it is equivalent to the linear equation π = θ1 − α. Similarly, the period-cohort plane can be thought of as a function of θ2 = π + γ, with π = θ2 − γ.
 
21
As a reviewer noted, some of these nonlinearities could be noise. One way to address possible noise is to smooth out the nonlinearities by setting the parameters for the higher-order nonlinearities to 0. Alternatively, one could use natural cubic splines, treating age, period, and cohort as continuous rather than categorical variables (see Heuer 1997).
 
22
The raw data are yearly, ranging from 1973 to 2013.
 
23
Nearly identical results were obtained with Poisson regression, but for ease of exposition, we present our findings using a classical linear regression with a logged rate outcome.
 
24
Estimated values of both θ1 and θ2 are statistically significant at the conventional threshold of .05.
 
25
F tests indicate that all three time scales have statistically significant nonlinearities at the conventional threshold of .05.
 
26
However, this interpretation is not strictly correct because we can estimate the values of θ1 and θ2. For example, if we assume that the period slope is 0, then the age slope must be 7.344 rather than 0.
 
27
Estimated values of both θ1 and θ2 are statistically significant at the conventional threshold of .05.
 
28
F tests show that at the conventional threshold of .05, all three time scales exhibit statistically significant nonlinearities.
 
29
The 2D-APC graph of the solution line is available in the online appendix.
 
Literatur
Zurück zum Zitat Alwin, D. F. (1991). Family of origin and cohort differences in verbal ability. American Sociological Review, 56, 625–638.CrossRef Alwin, D. F. (1991). Family of origin and cohort differences in verbal ability. American Sociological Review, 56, 625–638.CrossRef
Zurück zum Zitat Ames, B. N., Shigenaga, M. K., & Hagen, T. M. (1993). Oxidants, antioxidants, and the degenerative diseases of aging. Proceedings of the National Academy of Sciences, 90, 7915–7922.CrossRef Ames, B. N., Shigenaga, M. K., & Hagen, T. M. (1993). Oxidants, antioxidants, and the degenerative diseases of aging. Proceedings of the National Academy of Sciences, 90, 7915–7922.CrossRef
Zurück zum Zitat Bartels, L. M., & Jackman, S. (2014). A generational model of political learning. Electoral Studies, 33, 7–18.CrossRef Bartels, L. M., & Jackman, S. (2014). A generational model of political learning. Electoral Studies, 33, 7–18.CrossRef
Zurück zum Zitat Bell, A., & Jones, K. (2014b). Don’t birth cohorts matter? A commentary and simulation exercise on Reither, Hauser, and Yang’s (2009) age-period-cohort study of obesity. Social Science & Medicine, 101, 176–180.CrossRef Bell, A., & Jones, K. (2014b). Don’t birth cohorts matter? A commentary and simulation exercise on Reither, Hauser, and Yang’s (2009) age-period-cohort study of obesity. Social Science & Medicine, 101, 176–180.CrossRef
Zurück zum Zitat Bell, A., & Jones, K. (2015a). Age, period and cohort processes in longitudinal and life course analysis: A multilevel perspective. In C. Burton-Jeangros, S. Cullati, A. Sacker, & D. Blane (Eds.), A life course perspective on health trajectories and transitions (pp. 197–213). Cham, Switzerland: Springer.CrossRef Bell, A., & Jones, K. (2015a). Age, period and cohort processes in longitudinal and life course analysis: A multilevel perspective. In C. Burton-Jeangros, S. Cullati, A. Sacker, & D. Blane (Eds.), A life course perspective on health trajectories and transitions (pp. 197–213). Cham, Switzerland: Springer.CrossRef
Zurück zum Zitat Bell, A., & Jones, K. (2015b). Bayesian informative priors with Yang and Land’s age-period-cohort model. Quality & Quantity, 49, 255–266.CrossRef Bell, A., & Jones, K. (2015b). Bayesian informative priors with Yang and Land’s age-period-cohort model. Quality & Quantity, 49, 255–266.CrossRef
Zurück zum Zitat Braga, S. F. M., de Souza, M. C., & Cherchiglia, M. L. (2017). Time trends for prostate cancer mortality in Brazil and its geographic regions: An age-period-cohort analysis. Cancer Epidemiology, 50, 53–59.CrossRef Braga, S. F. M., de Souza, M. C., & Cherchiglia, M. L. (2017). Time trends for prostate cancer mortality in Brazil and its geographic regions: An age-period-cohort analysis. Cancer Epidemiology, 50, 53–59.CrossRef
Zurück zum Zitat Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. Cambridge, MA: National Bureau of Economic Research. Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. Cambridge, MA: National Bureau of Economic Research.
Zurück zum Zitat Chang, C.-K., Yu, H.-J., Chan, K.-W. A., & Lai, M.-K. (1997). Secular trend and age-period-cohort analysis of prostate cancer mortality in Taiwan. Journal of Urology, 158, 1845–1848.CrossRef Chang, C.-K., Yu, H.-J., Chan, K.-W. A., & Lai, M.-K. (1997). Secular trend and age-period-cohort analysis of prostate cancer mortality in Taiwan. Journal of Urology, 158, 1845–1848.CrossRef
Zurück zum Zitat Chaves, M. (1989). Secularization and religious revival: Evidence from U.S. church attendance rates, 1972–1986. Journal for the Scientific Study of Religion, 28, 464–477.CrossRef Chaves, M. (1989). Secularization and religious revival: Evidence from U.S. church attendance rates, 1972–1986. Journal for the Scientific Study of Religion, 28, 464–477.CrossRef
Zurück zum Zitat Chen, X., Li, G., Unger, J. B., Liu, X., & Johnson, C. A. (2003). Secular trends in adolescent never smoking from 1990 to 1999 in California: An age-period-cohort analysis. American Journal of Public Health, 93, 2099–2104.CrossRef Chen, X., Li, G., Unger, J. B., Liu, X., & Johnson, C. A. (2003). Secular trends in adolescent never smoking from 1990 to 1999 in California: An age-period-cohort analysis. American Journal of Public Health, 93, 2099–2104.CrossRef
Zurück zum Zitat Clark, A. K., & Eisenstein, M. A. (2013). Interpersonal trust: An age–period–cohort analysis revisited. Social Science Research, 42, 361–375.CrossRef Clark, A. K., & Eisenstein, M. A. (2013). Interpersonal trust: An age–period–cohort analysis revisited. Social Science Research, 42, 361–375.CrossRef
Zurück zum Zitat Clayton, D., & Schifflers, E. (1987). Models for temporal variation in cancer rates. II: Age-period-cohort models. Statistics in Medicine, 6, 469–481.CrossRef Clayton, D., & Schifflers, E. (1987). Models for temporal variation in cancer rates. II: Age-period-cohort models. Statistics in Medicine, 6, 469–481.CrossRef
Zurück zum Zitat Diouf, I., Charles, M. A., Ducimetière, P., Basdevant, A., Eschwege, E., & Heude, B. (2010). Evolution of obesity prevalence in France: An age-period-cohort analysis. Epidemiology, 21, 360–365.CrossRef Diouf, I., Charles, M. A., Ducimetière, P., Basdevant, A., Eschwege, E., & Heude, B. (2010). Evolution of obesity prevalence in France: An age-period-cohort analysis. Epidemiology, 21, 360–365.CrossRef
Zurück zum Zitat Farrington, D. P. (1986). Age and crime. Crime and Justice: A Review of Research, 7, 189–250.CrossRef Farrington, D. P. (1986). Age and crime. Crime and Justice: A Review of Research, 7, 189–250.CrossRef
Zurück zum Zitat Fienberg, S. E. (2013). Cohort analysis’ unholy quest: A discussion. Demography, 50, 1981–1984.CrossRef Fienberg, S. E. (2013). Cohort analysis’ unholy quest: A discussion. Demography, 50, 1981–1984.CrossRef
Zurück zum Zitat Fienberg, S. E., Hodges, J. S., & Luo, L. (2015). Letter to the editor. Journal of the American Statistical Association, 110, 457.CrossRef Fienberg, S. E., Hodges, J. S., & Luo, L. (2015). Letter to the editor. Journal of the American Statistical Association, 110, 457.CrossRef
Zurück zum Zitat Fienberg, S. E., & Mason, W. M. (1979). Identification and estimation of age-period-cohort models in the analysis of discrete archival data. Sociological Methodology, 10, 1–67.CrossRef Fienberg, S. E., & Mason, W. M. (1979). Identification and estimation of age-period-cohort models in the analysis of discrete archival data. Sociological Methodology, 10, 1–67.CrossRef
Zurück zum Zitat Firebaugh, G. (1989). Methods for estimating cohort replacement effects. Sociological Methodology, 19, 243–262.CrossRef Firebaugh, G. (1989). Methods for estimating cohort replacement effects. Sociological Methodology, 19, 243–262.CrossRef
Zurück zum Zitat Firebaugh, G., & Harley, B. (1991). Trends in U.S. church attendance: Secularization and revival or merely lifecycle effects? Journal for the Scientific Study of Religion, 30, 487–500.CrossRef Firebaugh, G., & Harley, B. (1991). Trends in U.S. church attendance: Secularization and revival or merely lifecycle effects? Journal for the Scientific Study of Religion, 30, 487–500.CrossRef
Zurück zum Zitat Frost, W. H. (1995). The age selection of mortality from tuberculosis in successive decades. American Journal of Epidemiology, 41, 4–9. (Reprinted from American Journal of Hygiene, 30, 91–96). Frost, W. H. (1995). The age selection of mortality from tuberculosis in successive decades. American Journal of Epidemiology, 41, 4–9. (Reprinted from American Journal of Hygiene, 30, 91–96).
Zurück zum Zitat Fryer, R. G. Jr., Heaton, P. S., Levitt, S. D., & Murphy, K. M. (2013). Measuring crack cocaine and its impact. Economic Inquiry, 51, 1651–1681.CrossRef Fryer, R. G. Jr., Heaton, P. S., Levitt, S. D., & Murphy, K. M. (2013). Measuring crack cocaine and its impact. Economic Inquiry, 51, 1651–1681.CrossRef
Zurück zum Zitat Fu, Q., & Land, K. C. (2015). The increasing prevalence of overweight and obesity of children and youth in China, 1989–2009: An age-period-cohort analysis. Population Research and Policy Review, 34, 901–921.CrossRef Fu, Q., & Land, K. C. (2015). The increasing prevalence of overweight and obesity of children and youth in China, 1989–2009: An age-period-cohort analysis. Population Research and Policy Review, 34, 901–921.CrossRef
Zurück zum Zitat Fu, W. J. (2000). Ridge estimator in singular design with application to age-period-cohort analysis of disease rates. Communications in Statistics—Theory and Methods, 29, 263–278.CrossRef Fu, W. J. (2000). Ridge estimator in singular design with application to age-period-cohort analysis of disease rates. Communications in Statistics—Theory and Methods, 29, 263–278.CrossRef
Zurück zum Zitat Fu, W. J. (2016). Constrained estimators and consistency of a regression model on a lexis diagram. Journal of the American Statistical Association, 111, 180–199.CrossRef Fu, W. J. (2016). Constrained estimators and consistency of a regression model on a lexis diagram. Journal of the American Statistical Association, 111, 180–199.CrossRef
Zurück zum Zitat Fu, W. J., Land, K. C., & Yang, Y. (2011). On the intrinsic estimator and constrained estimators in age-period-cohort models. Sociological Methods & Research, 40, 453–466.CrossRef Fu, W. J., Land, K. C., & Yang, Y. (2011). On the intrinsic estimator and constrained estimators in age-period-cohort models. Sociological Methods & Research, 40, 453–466.CrossRef
Zurück zum Zitat Ghitza, Y., & Gelman, A. (2014). The great society, Reagan’s revolution, and generations of presidential voting. Unpublished manuscript, Columbia University, New York, NY. Ghitza, Y., & Gelman, A. (2014). The great society, Reagan’s revolution, and generations of presidential voting. Unpublished manuscript, Columbia University, New York, NY.
Zurück zum Zitat Glenn, N. D. (1976). Cohort analysts’ futile quest: Statistical attempts to separate age, period and cohort effects. American Sociological Review, 41, 900–904.CrossRef Glenn, N. D. (1976). Cohort analysts’ futile quest: Statistical attempts to separate age, period and cohort effects. American Sociological Review, 41, 900–904.CrossRef
Zurück zum Zitat Glenn, N. D. (1981). The utility and logic of cohort analysis. Journal of Applied Behavioral Science, 17, 247–257. Glenn, N. D. (1981). The utility and logic of cohort analysis. Journal of Applied Behavioral Science, 17, 247–257.
Zurück zum Zitat Harman, D. (1956). Aging: A theory based on free radical and radiation chemistry. Journal of Gerontology, 11, 298–300.CrossRef Harman, D. (1956). Aging: A theory based on free radical and radiation chemistry. Journal of Gerontology, 11, 298–300.CrossRef
Zurück zum Zitat Hauser, R. M., & Huang, M.-H. (1997). Verbal ability and socioeconomic success: A trend analysis. Social Science Research, 26, 331–376.CrossRef Hauser, R. M., & Huang, M.-H. (1997). Verbal ability and socioeconomic success: A trend analysis. Social Science Research, 26, 331–376.CrossRef
Zurück zum Zitat Heuer, C. (1997). Modeling of time trends and interactions in vital rates using restricted regression splines. Biometrics, 53, 161–177.CrossRef Heuer, C. (1997). Modeling of time trends and interactions in vital rates using restricted regression splines. Biometrics, 53, 161–177.CrossRef
Zurück zum Zitat Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89, 552–584.CrossRef Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89, 552–584.CrossRef
Zurück zum Zitat Holford, T. R. (1983). The estimation of age, period and cohort effects for vital rates. Biometrics, 39, 311–324.CrossRef Holford, T. R. (1983). The estimation of age, period and cohort effects for vital rates. Biometrics, 39, 311–324.CrossRef
Zurück zum Zitat Holford, T. R. (1985). An alternative approach to statistical age-period-cohort analysis. Journal of Chronic Diseases, 38, 831–836.CrossRef Holford, T. R. (1985). An alternative approach to statistical age-period-cohort analysis. Journal of Chronic Diseases, 38, 831–836.CrossRef
Zurück zum Zitat Holford, T. R. (2006). Approaches to fitting age-period-cohort models with unequal intervals. Statistics in Medicine, 25, 977–993.CrossRef Holford, T. R. (2006). Approaches to fitting age-period-cohort models with unequal intervals. Statistics in Medicine, 25, 977–993.CrossRef
Zurück zum Zitat Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.CrossRef Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.CrossRef
Zurück zum Zitat Hout, M., & Knoke, D. (1975). Change in voting turnout, 1952–1972. Public Opinion Quarterly, 39, 52–68.CrossRef Hout, M., & Knoke, D. (1975). Change in voting turnout, 1952–1972. Public Opinion Quarterly, 39, 52–68.CrossRef
Zurück zum Zitat James, I. R., & Segal, M. R. (1982). On a method of mortality analysis incorporating age-year interaction, with application to prostate cancer mortality. Biometrics, 38, 433–443.CrossRef James, I. R., & Segal, M. R. (1982). On a method of mortality analysis incorporating age-year interaction, with application to prostate cancer mortality. Biometrics, 38, 433–443.CrossRef
Zurück zum Zitat Keiding, N. (2011). Age-period-cohort analysis in the 1870s: Diagrams, stereograms, and the basic differential equation. Canadian Journal of Statistics, 39(Special Issue), 405–420. Keiding, N. (2011). Age-period-cohort analysis in the 1870s: Diagrams, stereograms, and the basic differential equation. Canadian Journal of Statistics, 39(Special Issue), 405–420.
Zurück zum Zitat Kerr, W. C., Greenfield, T. K., Bond, J., Ye, Y., & Rehm, J. (2004). Age, period and cohort influences on beer, wine and spirits consumption trends in the U.S. National Alcohol Surveys. Addiction, 99, 1111–1120.CrossRef Kerr, W. C., Greenfield, T. K., Bond, J., Ye, Y., & Rehm, J. (2004). Age, period and cohort influences on beer, wine and spirits consumption trends in the U.S. National Alcohol Surveys. Addiction, 99, 1111–1120.CrossRef
Zurück zum Zitat Knight, K., & Fu, W. (2000). Asymptotics for lasso-type estimators. Annals of Statistics, 28, 1356–1378.CrossRef Knight, K., & Fu, W. (2000). Asymptotics for lasso-type estimators. Annals of Statistics, 28, 1356–1378.CrossRef
Zurück zum Zitat Land, K. C., Fu, Q., Guo, X., Jeon, S. Y., Reither, E. N., & Zang, E. (2016). Playing with the rules and making misleading statements: A response to Luo, Hodges, Winship, and Powers. American Journal of Sociology, 122, 962–973.CrossRef Land, K. C., Fu, Q., Guo, X., Jeon, S. Y., Reither, E. N., & Zang, E. (2016). Playing with the rules and making misleading statements: A response to Luo, Hodges, Winship, and Powers. American Journal of Sociology, 122, 962–973.CrossRef
Zurück zum Zitat Lavori, P. W., Klerman, G. L., Keller, M. B., Reich, T., Rice, J., & Endicott, J. (1987). Age-period-cohort analysis of secular trends in onset of major depression: Findings in siblings of patients with major affective disorder. Journal of Psychiatric Research, 21, 23–35.CrossRef Lavori, P. W., Klerman, G. L., Keller, M. B., Reich, T., Rice, J., & Endicott, J. (1987). Age-period-cohort analysis of secular trends in onset of major depression: Findings in siblings of patients with major affective disorder. Journal of Psychiatric Research, 21, 23–35.CrossRef
Zurück zum Zitat Lee, H. A., & Park, H. (2012). Trends in ischemic heart disease mortality in Korea, 1985–2009: An age-period-cohort analysis. Journal of Preventive Medicine and Public Health, 45 , 323–328.CrossRef Lee, H. A., & Park, H. (2012). Trends in ischemic heart disease mortality in Korea, 1985–2009: An age-period-cohort analysis. Journal of Preventive Medicine and Public Health, 45 , 323–328.CrossRef
Zurück zum Zitat Lewinsohn, P. M., Rohde, P., Seeley, J. R., & Fischer, S. A. (1993). Age-cohort changes in the lifetime occurrence of depression and other mental disorders. Journal of Abnormal Psychology, 102, 110–120.CrossRef Lewinsohn, P. M., Rohde, P., Seeley, J. R., & Fischer, S. A. (1993). Age-cohort changes in the lifetime occurrence of depression and other mental disorders. Journal of Abnormal Psychology, 102, 110–120.CrossRef
Zurück zum Zitat Liu, S., Semenciw, R., Ugnat, A.-M., & Mao, Y. (2001). Increasing thyroid cancer incidence in Canada, 1970–1996: Time trends and age-period-cohort effects. British Journal of Cancer, 85, 1335–1339.CrossRef Liu, S., Semenciw, R., Ugnat, A.-M., & Mao, Y. (2001). Increasing thyroid cancer incidence in Canada, 1970–1996: Time trends and age-period-cohort effects. British Journal of Cancer, 85, 1335–1339.CrossRef
Zurück zum Zitat Loeber, R., & Farrington, D. P. (2014). Age-crime curve. In G. Bruinsma & D. Weisburd (Eds.), Encyclopedia of criminology and criminal justice (pp. 12–18). New York, NY: Springer.CrossRef Loeber, R., & Farrington, D. P. (2014). Age-crime curve. In G. Bruinsma & D. Weisburd (Eds.), Encyclopedia of criminology and criminal justice (pp. 12–18). New York, NY: Springer.CrossRef
Zurück zum Zitat Luo, L. (2013). Assessing validity and application scope of the intrinsic estimator approach to the age-period-cohort problem. Demography, 50, 1945–1967.CrossRef Luo, L. (2013). Assessing validity and application scope of the intrinsic estimator approach to the age-period-cohort problem. Demography, 50, 1945–1967.CrossRef
Zurück zum Zitat Luo, L., Hodges, J., Winship, C., & Powers, D. (2016). The sensitivity of the intrinsic estimator to coding schemes: Comment on Yang, Schulhofer-Wohl, Fu, and Land. American Journal of Sociology, 122, 930–961.CrossRef Luo, L., Hodges, J., Winship, C., & Powers, D. (2016). The sensitivity of the intrinsic estimator to coding schemes: Comment on Yang, Schulhofer-Wohl, Fu, and Land. American Journal of Sociology, 122, 930–961.CrossRef
Zurück zum Zitat Mannheim, K. (1952). The sociological problem of generations. In P. Kecskemeti (Ed.), Essays on the sociology of knowledge (pp. 276–322). London, UK: Routledge & Kegan Paul. Mannheim, K. (1952). The sociological problem of generations. In P. Kecskemeti (Ed.), Essays on the sociology of knowledge (pp. 276–322). London, UK: Routledge & Kegan Paul.
Zurück zum Zitat Manski, C. F. (1990). Nonparametric bounds on treatment effects. American Economic Review: Papers & Proceedings, 80, 319–323. Manski, C. F. (1990). Nonparametric bounds on treatment effects. American Economic Review: Papers & Proceedings, 80, 319–323.
Zurück zum Zitat Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. Review of Economic Studies, 60, 531–542.CrossRef Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. Review of Economic Studies, 60, 531–542.CrossRef
Zurück zum Zitat Manski, C. F. (2003). Identification problems in the social sciences and everyday life. Southern Economic Journal, 70, 11–21.CrossRef Manski, C. F. (2003). Identification problems in the social sciences and everyday life. Southern Economic Journal, 70, 11–21.CrossRef
Zurück zum Zitat Mason, K. O., Mason, W. M., Winsborough, H. H., & Poole, W. K. (1973). Some methodological issues in cohort analysis of archival data. American Sociological Review, 38, 242–258.CrossRef Mason, K. O., Mason, W. M., Winsborough, H. H., & Poole, W. K. (1973). Some methodological issues in cohort analysis of archival data. American Sociological Review, 38, 242–258.CrossRef
Zurück zum Zitat Mason, W. M., & Fienberg, S. E. (Eds.). (1985). Cohort analysis in social research. New York, NY: Springer. Mason, W. M., & Fienberg, S. E. (Eds.). (1985). Cohort analysis in social research. New York, NY: Springer.
Zurück zum Zitat Mason, W. M., & Smith, H. L. (1985). Age-period-cohort analysis and the study of deaths from pulmonary tuberculosis. In W. M. Mason & S. E. Fienberg (Eds.), Cohort analysis in social research (pp. 151–227). New York, NY: Springer.CrossRef Mason, W. M., & Smith, H. L. (1985). Age-period-cohort analysis and the study of deaths from pulmonary tuberculosis. In W. M. Mason & S. E. Fienberg (Eds.), Cohort analysis in social research (pp. 151–227). New York, NY: Springer.CrossRef
Zurück zum Zitat Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference: Methods and principles for social research (2nd ed.). Cambridge, UK: Cambridge University Press. Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference: Methods and principles for social research (2nd ed.). Cambridge, UK: Cambridge University Press.
Zurück zum Zitat Nakamura, T. (1986). Bayesian cohort models for general cohort table analyses. Annals of the Institute of Statistical Mathematics, 38, 353–370.CrossRef Nakamura, T. (1986). Bayesian cohort models for general cohort table analyses. Annals of the Institute of Statistical Mathematics, 38, 353–370.CrossRef
Zurück zum Zitat Niclis, C., Pou, S. A., Bengió, R. H., Osella, A. R., & Díaz, M. d. P. (2011). Prostate cancer mortality trends in Argentina 1986–2006: An age-period-cohort and joinpoint analysis. Cadernos de Saúde Pública/Ministério da Saúde, Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública (Public Health Notebooks), 27, 123–130. https://doi.org/10.1590/S0102-311X2011000100013 CrossRef Niclis, C., Pou, S. A., Bengió, R. H., Osella, A. R., & Díaz, M. d. P. (2011). Prostate cancer mortality trends in Argentina 1986–2006: An age-period-cohort and joinpoint analysis. Cadernos de Saúde Pública/Ministério da Saúde, Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública (Public Health Notebooks), 27, 123–130. https://​doi.​org/​10.​1590/​S0102-311X201100010001​3 CrossRef
Zurück zum Zitat O’Brien, R. M. (2011a). The age-period-cohort conundrum as two fundamental problems. Quality and Quantity, 45, 1429–1444.CrossRef O’Brien, R. M. (2011a). The age-period-cohort conundrum as two fundamental problems. Quality and Quantity, 45, 1429–1444.CrossRef
Zurück zum Zitat O’Brien, R. M. (2011b). Constrained estimators and age-period-cohort models. Sociological Methods & Research, 40, 419–452.CrossRef O’Brien, R. M. (2011b). Constrained estimators and age-period-cohort models. Sociological Methods & Research, 40, 419–452.CrossRef
Zurück zum Zitat O’Brien, R. M. (2015a). Age-period-cohort models: Approaches and analyses with aggregate data. Boca Raton, FL: CRC Press. O’Brien, R. M. (2015a). Age-period-cohort models: Approaches and analyses with aggregate data. Boca Raton, FL: CRC Press.
Zurück zum Zitat O’Malley, P. M., Bachman, J. G., & Johnston, L. D. (1984). Period, age, and cohort effects on substance use among American youth, 1976–82. American Journal of Public Health, 74, 682–688.CrossRef O’Malley, P. M., Bachman, J. G., & Johnston, L. D. (1984). Period, age, and cohort effects on substance use among American youth, 1976–82. American Journal of Public Health, 74, 682–688.CrossRef
Zurück zum Zitat Palmore, E. (1978). When can age, period, and cohort be separated? Social Forces, 57, 282–295.CrossRef Palmore, E. (1978). When can age, period, and cohort be separated? Social Forces, 57, 282–295.CrossRef
Zurück zum Zitat Pearl, J. (2009). Causality. Cambridge, UK: Cambridge University Press.CrossRef Pearl, J. (2009). Causality. Cambridge, UK: Cambridge University Press.CrossRef
Zurück zum Zitat Pelzer, B., te Grotenhuis, M., Eisinga, R., & Schmidt-Catran, A. W. (2014). The non-uniqueness property of the intrinsic estimator in APC models. Demography, 52, 315–327.CrossRef Pelzer, B., te Grotenhuis, M., Eisinga, R., & Schmidt-Catran, A. W. (2014). The non-uniqueness property of the intrinsic estimator in APC models. Demography, 52, 315–327.CrossRef
Zurück zum Zitat Powers, D. A. (2013). Black-white differences in maternal age, maternal birth cohort, and period effects on infant mortality in the U.S. (1983–2002). Social Science Research, 42, 1033–1045.CrossRef Powers, D. A. (2013). Black-white differences in maternal age, maternal birth cohort, and period effects on infant mortality in the U.S. (1983–2002). Social Science Research, 42, 1033–1045.CrossRef
Zurück zum Zitat Pryor, W. A. (1982). Free radical biology: Xenobiotics, cancer, and aging. Annals of the New York Academy of Sciences, 393, 1–22.CrossRef Pryor, W. A. (1982). Free radical biology: Xenobiotics, cancer, and aging. Annals of the New York Academy of Sciences, 393, 1–22.CrossRef
Zurück zum Zitat Putnam, R. D. (1995). Tuning in, tuning out: The strange disappearance of social capital in America. PS: Political Science & Politics, 28, 664–683. Putnam, R. D. (1995). Tuning in, tuning out: The strange disappearance of social capital in America. PS: Political Science & Politics, 28, 664–683.
Zurück zum Zitat Reither, E. N., Hauser, R. M., & Yang, Y. (2009). Do birth cohorts matter? Age-period-cohort analyses of the obesity epidemic in the United States. Social Science & Medicine, 69, 1439–1448.CrossRef Reither, E. N., Hauser, R. M., & Yang, Y. (2009). Do birth cohorts matter? Age-period-cohort analyses of the obesity epidemic in the United States. Social Science & Medicine, 69, 1439–1448.CrossRef
Zurück zum Zitat Riebler, A., & Held, L. (2017). Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. Biometrical Journal, 59, 531–549.CrossRef Riebler, A., & Held, L. (2017). Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. Biometrical Journal, 59, 531–549.CrossRef
Zurück zum Zitat Robinson, R. V., & Jackson, E. F. (2001). Is trust in others declining in America? An age-period-cohort analysis. Social Science Research, 30, 117–145.CrossRef Robinson, R. V., & Jackson, E. F. (2001). Is trust in others declining in America? An age-period-cohort analysis. Social Science Research, 30, 117–145.CrossRef
Zurück zum Zitat Rodgers, W. L. (1982a). Estimable functions of age, period, and cohort effects. American Sociological Review, 47, 774–787.CrossRef Rodgers, W. L. (1982a). Estimable functions of age, period, and cohort effects. American Sociological Review, 47, 774–787.CrossRef
Zurück zum Zitat Rodgers, W. L. (1982b). Reply to comment by Smith, Mason, and Fienberg. American Sociological Review, 47, 793–796.CrossRef Rodgers, W. L. (1982b). Reply to comment by Smith, Mason, and Fienberg. American Sociological Review, 47, 793–796.CrossRef
Zurück zum Zitat Rodgers, W. L. (1990). Interpreting the components of time trends. Sociological Methodology, 20, 421–438.CrossRef Rodgers, W. L. (1990). Interpreting the components of time trends. Sociological Methodology, 20, 421–438.CrossRef
Zurück zum Zitat Rubin, D. B. (1986). Comment: Which ifs have causal answers. Journal of the American Statistical Association, 81, 961–962. Rubin, D. B. (1986). Comment: Which ifs have causal answers. Journal of the American Statistical Association, 81, 961–962.
Zurück zum Zitat Ryder, N. B. (1965). The cohort as a concept in the study of social change. American Sociological Review, 30, 843–861.CrossRef Ryder, N. B. (1965). The cohort as a concept in the study of social change. American Sociological Review, 30, 843–861.CrossRef
Zurück zum Zitat Schwadel, P., & Stout, M. (2012). Age, period and cohort effects on social capital. Social Forces, 91, 233–252.CrossRef Schwadel, P., & Stout, M. (2012). Age, period and cohort effects on social capital. Social Forces, 91, 233–252.CrossRef
Zurück zum Zitat Siegel, R. L., Fedewa, S. A., Anderson, W. F., Miller, K. D., Ma, J., Rosenberg, P. S., & Jemal, A. (2017). Colorectal cancer incidence patterns in the United States, 1974–2013. JNCI: Journal of the National Cancer Institute, 109(8), djw322. https://doi.org/10.1093/jnci/djw322 CrossRef Siegel, R. L., Fedewa, S. A., Anderson, W. F., Miller, K. D., Ma, J., Rosenberg, P. S., & Jemal, A. (2017). Colorectal cancer incidence patterns in the United States, 1974–2013. JNCI: Journal of the National Cancer Institute, 109(8), djw322. https://​doi.​org/​10.​1093/​jnci/​djw322 CrossRef
Zurück zum Zitat Smith, H. L., Mason, W. M., & Fienberg, S. E. (1982). Estimable functions of age, period, and cohort effects: More chimeras of the age-period-cohort accounting framework: Comment on Rodgers. American Sociological Review, 47, 787–793.CrossRef Smith, H. L., Mason, W. M., & Fienberg, S. E. (1982). Estimable functions of age, period, and cohort effects: More chimeras of the age-period-cohort accounting framework: Comment on Rodgers. American Sociological Review, 47, 787–793.CrossRef
Zurück zum Zitat Smith, T. R., & Wakefield, J. (2016). A review and comparison of age-period-cohort models for cancer incidence. Statistical Science, 31, 591–610.CrossRef Smith, T. R., & Wakefield, J. (2016). A review and comparison of age-period-cohort models for cancer incidence. Statistical Science, 31, 591–610.CrossRef
Zurück zum Zitat Tilley, J., & Evans, G. (2014). Ageing and generational effects on vote choice: Combining cross-sectional and panel data to estimate APC effects. Electoral Studies, 33, 19–27.CrossRef Tilley, J., & Evans, G. (2014). Ageing and generational effects on vote choice: Combining cross-sectional and panel data to estimate APC effects. Electoral Studies, 33, 19–27.CrossRef
Zurück zum Zitat Ulmer, J. T., & Steffensmeier, D. (2014). The age and crime relationship: Social variation, social explanations. In K. M. Beaver, J. C. Barnes, & B. B. Boutwell (Eds.), The nurture versus biosocial debate in criminology: On the origins of criminal behavior and criminality (pp. 377–396). Newbury Park, CA: Sage.CrossRef Ulmer, J. T., & Steffensmeier, D. (2014). The age and crime relationship: Social variation, social explanations. In K. M. Beaver, J. C. Barnes, & B. B. Boutwell (Eds.), The nurture versus biosocial debate in criminology: On the origins of criminal behavior and criminality (pp. 377–396). Newbury Park, CA: Sage.CrossRef
Zurück zum Zitat Vedøy, T. F. (2014). Tracing the cigarette epidemic: An age-period-cohort study of education, gender and smoking using a pseudo-panel approach. Social Science Research, 48, 35–47.CrossRef Vedøy, T. F. (2014). Tracing the cigarette epidemic: An age-period-cohort study of education, gender and smoking using a pseudo-panel approach. Social Science Research, 48, 35–47.CrossRef
Zurück zum Zitat Wilson, J. A., & Gove, W. R. (1999). The age-period-cohort conundrum and verbal ability: Empirical relationships and their interpretation: Reply to Glenn and to Alwin and McCammon. American Sociological Review, 64, 287–302.CrossRef Wilson, J. A., & Gove, W. R. (1999). The age-period-cohort conundrum and verbal ability: Empirical relationships and their interpretation: Reply to Glenn and to Alwin and McCammon. American Sociological Review, 64, 287–302.CrossRef
Zurück zum Zitat Winship, C., & Harding, D. J. (2008). A mechanism-based approach to the identification of age-period-cohort models. Sociological Methods & Research, 36, 362–401.CrossRef Winship, C., & Harding, D. J. (2008). A mechanism-based approach to the identification of age-period-cohort models. Sociological Methods & Research, 36, 362–401.CrossRef
Zurück zum Zitat Yang, Y. (2008). Social inequalities in happiness in the United States, 1972 to 2004: An age-period-cohort analysis. American Sociological Review, 73, 204–226.CrossRef Yang, Y. (2008). Social inequalities in happiness in the United States, 1972 to 2004: An age-period-cohort analysis. American Sociological Review, 73, 204–226.CrossRef
Zurück zum Zitat Yang, Y., Fu, W. J., & Land, K. C. (2004). A methodological comparison of age-period-cohort models: The intrinsic estimator and conventional generalized linear models. Sociological Methodology, 34, 75–110.CrossRef Yang, Y., Fu, W. J., & Land, K. C. (2004). A methodological comparison of age-period-cohort models: The intrinsic estimator and conventional generalized linear models. Sociological Methodology, 34, 75–110.CrossRef
Zurück zum Zitat Yang, Y., & Land, K. C. (2006). A mixed models approach to the age-period-cohort analysis of repeated cross-section surveys, with an application to data on trends in verbal test scores. Sociological Methodology, 36, 75–97.CrossRef Yang, Y., & Land, K. C. (2006). A mixed models approach to the age-period-cohort analysis of repeated cross-section surveys, with an application to data on trends in verbal test scores. Sociological Methodology, 36, 75–97.CrossRef
Zurück zum Zitat Yang, Y., & Land, K. C. (2013). Age-period-cohort analysis: New models, methods, and empirical applications. Boca Raton, FL: CRC Press. Yang, Y., & Land, K. C. (2013). Age-period-cohort analysis: New models, methods, and empirical applications. Boca Raton, FL: CRC Press.
Metadaten
Titel
Bounding Analyses of Age-Period-Cohort Effects
verfasst von
Ethan Fosse
Christopher Winship
Publikationsdatum
28.08.2019
Verlag
Springer US
Erschienen in
Demography / Ausgabe 5/2019
Print ISSN: 0070-3370
Elektronische ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-019-00801-6

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