A Practical Guide to Age-Period-Cohort Analysis

The Identification Problem and Beyond

Wenjiang Fu (Autor)

Buch | Hardcover

250 Seiten

2018
Crc Press Inc (Verlag)
978-1-4665-9265-0 (ISBN)

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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.

Wenjiang Fu

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

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 Age - Period - Cohort Data - Graphic Methods

D Plots in Age, Period, and Cohort

D Plot in Age, Period, and Cohort

Suggested Readings

Exercises

3. Preliminary Analysis of Age - Period - Cohort 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

R Programming for Loglinear Models

Suggested Readings

Exercises

4. Age-Period-Cohort Model - Complexity with Linearly Dependent Covariates

Lexis Diagram and Pattern 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 Model

Regression with Linearly Dependent Covariates

Age-Period-Cohort Models and the Complexity

R Programming for Generating the Design Matrix for APC Models

Suggested Readings

Exercises

5. Age-Period-Cohort Model - The Identification Problem and Various 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. The Intrinsic Estimator, the Rationale and Properties

Structure of Multiple Estimators of Age-Period-Cohort Models

Intrinsic Estimator - Unbiased Estimation and Other Properties

Robust Estimation via Sensitivity Analysis

Summary of Asymptotic Properties of the Multiple Estimators

Computation of the Intrinsic Estimator and Standard Errors

Computation of the Intrinsic Estimator

Computation of the Standard Errors

Suggested Readings

Exercises

7. Data Analysis with Intrinsic Estimator and Comparison with Others

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 the 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

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 Cohort 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

10. Unequal Spans in Age Groups and Periods with Applications to Survey Data

APC Data with Unequal Spans

The Intend-to-Collapse (ITC) Method

APC Models for Unequal Spans

Identification Problem and Intrinsic Estimator for Unequal Span Data

Multiple Estimators and Identification Problem

The Intrinsic Estimator for Unequal Span Data

Analyzing APC Data with Unequal Spans by the Intrinsic Estimator

Fitting Unequal Span Data with R Function apclinkfit

Exercises

Bibliography

Zusatzinfo	50 Illustrations, black and white
Verlagsort	Bosa Roca
Sprache	englisch
Maße	156 x 234 mm
Gewicht	1150 g
Themenwelt	Geisteswissenschaften ► Psychologie
	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
	Naturwissenschaften ► Biologie
	Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung
ISBN-10	1-4665-9265-6 / 1466592656
ISBN-13	978-1-4665-9265-0 / 9781466592650
Zustand	Neuware