Download Age-Period-Cohort Models: Approaches and Analyses with by Robert O'Brien PDF

By Robert O'Brien

Age-Period-Cohort versions: methods and Analyses with mixture info provides an creation to the issues and techniques for modeling age, interval, and cohort (APC) results for aggregate-level info. those innovations contain limited estimation, using age and/or interval and/or cohort features, estimable services, variance decomposition, and a brand new method referred to as the s-constraint approach.

summary: Age-Period-Cohort types: techniques and Analyses with mixture facts provides an advent to the issues and methods for modeling age, interval, and cohort (APC) results for aggregate-level info. those suggestions comprise restricted estimation, using age and/or interval and/or cohort features, estimable capabilities, variance decomposition, and a brand new process referred to as the s-constraint strategy

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1965. The cohort as a concept in the study of social change. American Sociological Review 30:843–61. , E. Allan, M. Harer, and C. Streifel. 1989. Age and the distribution of crime. American Journal of Sociology 94:803–31. M. O’Brien. 2002. Cohort effects on suicide rates: International variations. American Sociological Review 67:854–72. Vandeschrick, C. 2001. The Lexis diagram, a misnomer. Demographic Research 4:97–124. R. Gove. 1999. The intercohort decline in verbal ability: Does it exist? American Sociological Review 64:253–66.

I then show how constrained solutions force the hyperplane that does not intersect the line of solutions to be reoriented to intersect the line of solutions at a point and thus provide a solution to the APC model under a specified constraint. Chapter 3 reinforces and extends our conception of what a constrained solution is and how it is obtained. * The line of solutions discussed in Chapters 2 and 3 plays a prominent role in Chapter 4. Chapter 4 introduces estimable functions. Although the individual age group, period, and cohort parameter estimates in the APC model cannot be solved uniquely (in terms of providing estimates of the parameters that generated the outcome values), they can be used to provide solutions to certain functions of these parameters.

This generalizes, the constraint vector imposes the following requirement on the solution vector: the dot product of the constraint vector with the solution * Throughout this chapter we consider only single constraints that just identify the APC model. 33 Multiple Classification Models and Constrained Regression vector equals zero. Remembering that two vectors are orthogonal if their dot product is zero, we can write c′bc0 = 0. 8 Examining the Relationship between Solutions For concreteness, an empirical example is used to illustrate several of the points related to the characteristics of constrained solutions in APC models.

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