A Practical Guide to Age-Period-Cohort Analysis

The Identification Problem and Beyond

Wenjiang Fu

• Publisher: CRC Press
• Year: 2018
• eBook ISBN: 9781351644143
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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.

Contents

Preface

List of Figures

List of Figures

I Age-Period-Cohort Models, Challenges, Methods, and Rationale

1 Motivation of Age-Period-Cohort Analysis — Examples and Applications

1.1 What Is Age-Period-Cohort Analysis?

1.2 Why Age-Period-Cohort Analysis?

1.3 Four Data Sets in APC Studies

1.3.1 Special Features of These Data Sets

1.4 Data Source

1.5 R Programming and Video Online Instruction

1.6 Suggested Readings

1.7 Exercises

2 Preliminary Analysis — Graphic Methods

2.1 2D Plots in Age, Period, and Cohort

2.2 3D Plots in Age, Period, and Cohort

2.3 Suggested Readings

2.4 Exercises

3 Preliminary Analysis of Age-Period-Cohort Data — Basic Models

3.1 Linear Models for Continuous Response

3.1.1 Single Factor Models

3.1.2 Two Factor Models

3.1.3 R Programming for Linear Models

3.2 Loglinear Models for Discrete Response

3.2.1 Single Factor Models

3.2.2 Two Factor Models

3.2.3 Modeling Over-Dispersion with Quasi-Likelihood

3.2.4 R Programming for Loglinear Models

3.3 Suggested Readings

3.4 Exercises

4 Age-Period-Cohort Models — Complexity with Linearly Dependent Covariates

4.1 Lexis Diagram and Patterns in Age, Period, and Cohort

4.1.1 Lexis Diagram and Dependence among Age, Period, and Cohort

4.1.2 Explicit Pattern in APC Data with Identical Spans in Age and Period

4.1.3 Implicit Pattern in APC Data with Unequal Spans in Age and Period

4.2 Complexity in Full Age-Period-Cohort Models

4.2.1 Regression with Linearly Dependent Covariates

4.2.2 Age-Period-Cohort Models and Complexity

4.3 R Programming for Generating the Design Matrix for APC Models

4.4 Suggested Readings

4.5 Exercises

5 Age-Period-Cohort Models — The Identification Problem and Approaches

5.1 The Identification Problem and Confusion

5.2 Two Popular Approaches to the Identification Problem

5.2.1 Constraint Approach

5.2.2 Estimable Function Approach

5.3 Other Approaches to the Identification Problem

5.4 Suggested Readings

5.5 Exercises

6 Intrinsic Estimator, the Rationale and Properties

6.1 Structure of Multiple Estimators of Age-Period-Cohort Models

6.2 Intrinsic Estimator: Unbiased Estimates and Other Properties

6.3 Robust Estimation via Sensitivity Analysis

6.4 Summary of Asymptotic Properties of the Multiple Estimators

6.5 Computation of Intrinsic Estimator and Standard Errors

6.5.1 Computation of Intrinsic Estimator

6.5.2 Computation of Standard Errors

6.6 Suggested Readings

6.7 Exercises

7 Data Analysis with Intrinsic Estimator and Comparison with Other Methods

7.1 Illustration of Data Analysis with the Intrinsic Estimator

7.1.1 Modeling Lung Cancer Mortality Data among US Males

7.1.1.1 Intrinsic Estimator of Linear Models

7.1.1.2 Intrinsic Estimator of Loglinear Models

7.1.2 Modeling the HIV Mortality Data

7.1.2.1 Intrinsic Estimator of Linear Models

7.1.2.2 Intrinsic Estimator of Loglinear Models

7.2 Illustration of Data Analysis with Constrained Estimators

7.2.1 Illustration of Equality Constraints

7.2.2 Illustration of Non...

Table of contents

• Cover
• Halftitle
• Title Page
• Copyright Page
• Table of Contents