Practical guide to age-period cohort analysis : the identification problem and beyond

Content: 1. Motivation of APC Analysis What Is Age-Period-Cohort Analysis? Why Age-Period-Cohort Analysis? Four Data Sets in APC Studies Special Features of These Data Sets Data Source R Programming and Video Online Instruction Suggested Readings Exercises 2. Preliminary Analysis of APC Data | Graphic Methods D Plots in Age, Period, and Cohort D Plots in Age, Period, and Cohort Suggested Readings Exercises 3. Preliminary Analysis of APC Data | Basic Models Linear Models for Continuous Response Single Factor Models Two Factor Models R Programming for Linear Models Loglinear Models for Discrete Response Single Factor Models Two Factor Models Modeling Over-dispersion with Quasi-likelihood R Programming for Loglinear Models Suggested Readings Exercises 4. APC Models | Complexity with Linearly Dependent Co-variates Lexis Diagram and Patterns in Age, Period, and Cohort Lexis Diagram and Dependence among Age, Period, and Cohort Explicit Pattern in APC Data with Identical Spans in Age and Period Implicit Pattern in APC Data with Unequal Spans in Age and Period Complexity in Full Age-Period-Cohort Models Regression with Linearly Dependent Covariates Age-Period-Cohort Models and Complexity R Programming for Generating the Design Matrix for APC Models Suggested Readings Exercises 5. APC Models | The Identi_cation Problem and Approaches The Identification Problem and Confusion Two Popular Approaches to the Identification Problem Constraint Approach Estimable Function Approach Other Approaches to the Identification Problem Suggested Readings Exercises 6. Intrinsic Estimator, the Rationale and Properties Structure of Multiple Estimators Intrinsic Estimator: Unbiased Estimates and Other Properties Robust Estimation via Sensitivity Analysis Summary of Asymptotic Properties of the Multiple Estimators Computation of Intrinsic Estimator and Standard Errors Computation of Intrinsic Estimator Computation of Standard Errors Suggested Readings Exercises 7. Data Analysis with Intrinsic Estimator and Comparison Illustration of Data Analysis with the Intrinsic Estimator Modeling Lung Cancer Mortality Data among US Males Intrinsic Estimator of Linear Models Intrinsic Estimator of Loglinear Models Modeling the HIV Mortality Data Intrinsic Estimator of Linear Models Intrinsic Estimator of Loglinear Models Illustration of Data Analysis with Constrained Estimators Illustration of Equality Constraints Illustration of Non-contrast Constraints Suggested Readings Exercises 8. Asymptotic Behavior of Multiple Estimators | Theoretical Results Settings and Strategies to Study the Asymptotics of MultipleEstimators Assumptions and Regularity Conditions for the Asymptotics Asymptotics of Multiple Estimators Asymptotics of Multiple Estimators with Fixed t Asymptotics of Linearly Constrained Estimators Linear constraint on age effects Linear constraint on period or cohort effects Estimability of Intrinsic Estimator Suggested Readings Exercises 9. Variance Estimation and Selection of Side Condition Variance Estimation of the Intrinsic Estimator The Delta Method for the Variance of Period and Co-hort Effect Estimates Comparison of Standard Errors between the PCA and Delta Methods Selection of Side Condition Side Conditions for One-way ANOVA Models Side Conditions for Two-way ANOVA Models Side Conditions for Age-Period-Cohort Models Conclusion on Side Condition Selection Suggested Readings Exercises 10. Unequal Spans in Age and Period Groups APC Data with Unequal Spans The Intend-to-Collapse (ITC) Method APC Models for Unequal Spans Identi_cation Problem and Intrinsic Estimator for Unequal Span Data Multiple Estimators and Identi_cation Problem The Intrinsic Estimator for Unequal Span Data Analysis of Unequal Span Data Fitting Unequal Span Data with R Function apclinkfit Exercises Bibliography Index