A Practical Guide to Age-Period-Cohort Analysis: The Identification Problem and Beyond, 1st Edition (Hardback) book cover

A Practical Guide to Age-Period-Cohort Analysis

The Identification Problem and Beyond, 1st Edition

By Wenjiang Fu

Chapman and Hall/CRC

230 pages | 50 B/W Illus.

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pub: 2018-04-25
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Description

Age-Period-Cohort analysis has a wide range of applications, from chronic disease incidence and mortality data in public health and epidemiology, to many social events (birth, death, marriage, etc) in social sciences and demography, and most recently investment, healthcare and pension contribution in economics and finance. Although APC analysis has been studied for the past 40 years and a lot of methods have been developed, the identification problem has been a major hurdle in analyzing APC data, where the regression model has multiple estimators, leading to indetermination of parameters and temporal trends. A Practical Guide to Age-Period Cohort Analysis: The Identification Problem and Beyond provides practitioners a guide to using APC models as well as offers graduate students and researchers an overview of the current methods for APC analysis while clarifying the confusion of the identification problem by explaining why some methods address the problem well while others do not.

Features

· Gives a comprehensive and in-depth review of models and methods in APC analysis.

· Provides an in-depth explanation of the identification problem and statistical approaches to addressing the problem and clarifying the confusion.

· Utilizes real data sets to illustrate different data issues that have not been addressed in the literature, including unequal intervals in age and period groups, etc.

  • Contains step-by-step modeling instruction and R programs to demonstrate how to conduct APC analysis and how to conduct prediction for the future

  • Reflects the most recent development in APC modeling and analysis including the intrinsic estimator
  • Wenjiang Fu is a professor of statistics at the University of Houston. Professor Fu’s research interests include modeling big data, applied statistics research in health and human genome studies, and analysis of complex economic and social science data.

    Reviews

    "The book is intended for graduates and researchers: biostatisticians, public health economists and actuaries will find this book relevant to their work and even researchers on demography and life tables will benefit." ~Royal Statistical Society

    Table of Contents

    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

    RProgramming 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 Multiple

    Estimators

    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

    Subject Categories

    BISAC Subject Codes/Headings:
    MAT029000
    MATHEMATICS / Probability & Statistics / General
    REF000000
    REFERENCE / General