Age-period-cohort models : approaches and analyses with aggregate data /

Age-period-cohort models : approaches and analyses with aggregate data /

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Bibliographic Details
Author / Creator:O'Brien, Robert M., author.
Imprint:Boca Raton, FL : CRC Press, Taylor & Francis Group, [2015]
Description:xi, 204 pages ; 24 cm.
Language:English
Series:Chapman & Hall/CRC statistics in the social and behavioral sciences series
Statistics in the social and behavioral sciences series.
Subject:
Cohort analysis.
Age groups -- Statistical models.
Cohort analysis.
Social sciences -- Statistical methods.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/10114837
Hidden Bibliographic Details
ISBN:1466551534
9781466551534
Notes:Includes bibliographical references and index.
Table of Contents:
  • Preface
  • Author
  • 1. Introduction to the Age, Period, and Cohort Mix
  • 1.1. Introduction
  • 1.2. Interest in Age, Period, and Cohort
  • 1.2.1. Age Alone
  • 1.2.2. Period Alone
  • 1.2.3. Cohort Alone
  • 1.2.4. Age-Period Explanation
  • 1.2.5. Age-Cohort Explanation
  • 1.2.6. Age-Period-Cohort Explanation
  • 1.3. Importance of Cohorts
  • 1.3.1. Life Table
  • 1.3.2. Lexis Diagram and the Coding of Cohorts
  • 1.3.3. Frost's Paper
  • 1.3.4. Cohorts as Engines of Social Change
  • 1.3.5. Concluding Remarks
  • 1.4. Plan for the Book
  • References
  • 2. Multiple Classification Models and Constrained Regression
  • 2.1. Introduction
  • 2.2. Linearly Coded Age-Period-Cohort (APC) Model
  • 2.3. Categorically Coded APC Model
  • 2.4. Generalized Linear Models
  • 2.5. Null Vector
  • 2.6. Model Fit
  • 2.7. Solution Is Orthogonal to the Constraint
  • 2.8. Examining the Relationship between Solutions
  • 2.9. Differences between Constrained Solutions as Rotations of Solutions
  • 2.10. Solutions Ignoring One or More of the Age, Period, or Cohort Factors
  • 2.11. Bias: Constrained Estimates and the Data Generating Parameters
  • 2.12. Unbiased Estimation under a Constraint
  • 2.13. A Plausible Constraint with Some Extra Empirical Support
  • 2.14. Conclusions
  • Appendix 2.1. Dummy Variable and Effect Coding
  • Appendix 2.2. Determining Null Vectors for Effect and Dummy Variable Coded Variables
  • Appendix 2.3. Constrained Estimates as Unbiased Estimates
  • References
  • 3. Geometry of Age-Period-Cohort (APC) Models and Constrained Estimation
  • 3.1. Introduction
  • 3.2. General Geometric View of Rank Deficient by One Models
  • 3.3. Generalization to Systems with More Dimensions
  • 3.4. APC Model with Linearly Coded Variables
  • 3.4.1. Age, Period, and Cohort as Continuous Variables: A Concrete Example
  • 3.4.2. Geometry of Age, Period, and Cohort for Linearly Coded Effects
  • 3.5. Equivalence of the Geometric and Algebraic Solutions
  • 3.6. Geometry of the Multiple Classification Model
  • 3.7. Distance from Origin and Distance along the Line of Solutions
  • 3.8. Empirical Example: Frost's Tuberculosis Data
  • 3.9. Summarizing Some Important Features from the Geometry of APC Models
  • 3.9.1. Solutions Lie on a Line in Multidimensional Space
  • 3.9.2. Distances as Geometric Insights
  • 3.9.3. Understanding How Constrained Regression Solves the Rank Deficient Case
  • 3.10. Problem with Mechanical Constraints
  • 3.11. Discussion
  • Appendix 3.1.
  • References
  • 4. Estimable Functions Approach
  • 4.1. Introduction
  • 4.2. Estimable Functions
  • 4.3. l'sv Approach for Establishing Estimable Functions in Age-Period-Cohort (APC) Models
  • 4.4. Some Examples of Estimable Functions Derived Using the l'sv Approach
  • 4.4.1. Effect Coefficients
  • 4.4.2. Second Differences
  • 4.4.3. Relationships between Slopes
  • 4.4.4. Change of Slope within Factors
  • 4.4.5. Deviations from Linearity
  • 4.4.6. Predicted Values of y
  • 4.5. Comments on the l'sv Approach
  • 4.6. Estimable Functions with Empirical Data
  • 4.7. More Substantive Examination of Differences of Male and Female Lung Cancer Mortality Rates
  • 4.8. Conclusions
  • Appendix 4.1.
  • References
  • 5. Partitioning the Variance in Age-Period-Cohort (APC) Models
  • 5.1. Introduction
  • 5.2. Age-Period-Cohort Analysis of Variance (APC ANOVA) Approach to Attributing Variance
  • 5.3. APC Mixed Model
  • 5.4. Hierarchical APC Model
  • 5.5. Empirical Example Using Homicide Offending Data
  • 5.5.1. Applying the APC ANOVA Approach
  • 5.5.2. Applying the APCMM Approach
  • 5.6. Conclusions
  • References
  • 6. Factor-Characteristic Approach
  • 6.1. Introduction
  • 6.2. Characteristics for One Factor
  • 6.2.1. Basic Model
  • 6.2.2. Problem of Specifying the Linear Relationship
  • 6.3. Characteristics for Two or More Factors
  • 6.4. Variance Decomposition for Factors and for Factor Characteristics
  • 6.5. Empirical Examples: Age-Period-Specific Suicide Rates and Frequencies
  • 6.6. Age-Period-Cohort Characteristics (APCC) Analysis of Suicide Data with Two Cohort Characteristics
  • 6.7. Age-Cohort-Period Characteristics (ACPC) Analysis of the Suicide Data with Two Period Characteristics
  • 6.8. Age-Period-Characteristics-Cohort Characteristics Model
  • 6.9. Approaches Based on Factor Characteristics and Mechanism
  • 6.10. Additional Features and Analyses of Factor-Characteristic Models
  • 6.11. Conclusions
  • References
  • 7. Conclusions: An Empirical Example
  • 7.1. Introduction
  • 7.2. Empirical Example: Homicide Offending
  • 7.2.1. Unique Variance and Deviations from Linearity
  • 7.2.2. Constrained Regression Using the s-Constraint Approach
  • 7.2.3. Estimating the Homicide Offending Mode! with Cohort Characteristics
  • 7.2.4. Conclusions for the Homicide Rate Analysis
  • 7.3. Conclusions
  • References
  • Index
Gift of the Clinton M. and Dorothy R. Doede Book Fund bookplate

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