A Practical Guide to Age Period Cohort Analysis 1st edition by Wenjiang Fu – Ebook PDF Instant Download/Delivery: 1466592656 , 978-1466592650
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ISBN 10: 1466592656
ISBN 13: 978-1466592650
Author: Wenjiang Fu
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.
A Practical Guide to Age Period Cohort Analysis 1st Table of contents:
PART 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-Contrast Constraints
7.3 Suggested Readings
7.4 Exercises
PART II A Resolution to the Identification Problem: Theoretical Justification and Related Topics
8 Asymptotic Behavior of Multiple Estimators — Theoretical Results
8.1 Settings and Strategies to Study the Asymptotics of Multiple Estimators
8.2 Assumptions and Regularity Conditions for the Asymptotics
8.3 Asymptotics of Multiple Estimators
8.3.1 Asymptotics of Multiple Estimators with Fixed t
8.3.2 Asymptotics of Linearly Constrained Estimators
8.3.2.1 Linear Constraint on Age Effects
8.3.2.2 Linear Constraint on Period or Cohort Effects
8.4 Estimability of Intrinsic Estimator
8.5 Suggested Readings
8.6 Exercises
9 Variance Estimation and Selection of Side Conditions
9.1 Variance Estimation of the Intrinsic Estimator
9.1.1 The Delta Method for the Variance of Period and Cohort Effect Estimates
9.1.2 Comparison of Standard Errors between the PCA and Delta Methods
9.2 Selection of Side Conditions
9.2.1 Side Conditions for One-Way ANOVA Models
9.2.2 Side Conditions for Two-Way ANOVA Models
9.2.3 Side Conditions for Age-Period-Cohort Models
9.2.4 Conclusion on Side Condition Selection
9.3 Suggested Readings
9.4 Exercises
10 Unequal Spans in Age and Period Groups with Applications to Survey Data
10.1 APC Data with Unequal Spans
10.2 The Intend-to-Collapse (ITC) Method
10.3 APC Models for Unequal Spans
10.4 Identification Problem and Intrinsic Estimator for Unequal Span Data
10.4.1 Multiple Estimators and Identification Problem
10.4.2 The Intrinsic Estimator for Unequal Span Data
10.4.3 Analysis of Unequal Span Data by Intrinsic Estimator
10.5 Fitting Unequal Span Data with R Function apclinkfit
10.6 Exercises
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