Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting. The frameworks draw on recent advances in statistical methodology to provide new tools for tackling challenges such as disaggregation, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems. The methods unify estimation and forecasting, and yield detailed measures of uncertainty.
The book assumes minimal knowledge of statistics, and no previous knowledge of demography. The authors have developed a set of R packages implementing the methods. Data and code for all applications in the book are available on www.bdef-book.com.
"This book will be welcome for the scientific community of forecasters…as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future." ~Daniel Courgeau, Institut national d'études démographiques
Table of Contents:
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- Introduction
Example: Mortality Rates for Maori
Our Approach to Demographic Estimation and Forecasting
Outline of the Rest of the Book
References and Further Reading
- Demographic Foundations
Demographic Foundations
References and Further Reading
Demographic Individuals
Attributes
Events
The Lexis Diagram
Twelve Fictitious Individuals
References and Further Reading
Demographic Arrays
Population Counts
Death Counts
Movements
Alternative Representations of Changing Statuses
Non-Demographic Events
Exposure
Age, Period, and Cohort
Rates, Proportions, Means, and Ratios
Super-Population and Finite-Population Quantities
Collapsing Dimensions
References and Further Reading
Demographic Accounts
Demographic Systems
Demographic Accounts
An Account with No Region and No Age
An Account with Region but Not Age
An Account with Age But Not Region
Movements Accounts and Transitions Accounts*
Mathematical Description of the Demographic Accounting Identities*
References and Further Reading
Demographic Data
Traditional Data Sources
New Data Sources
Data Quality and Model Choice
References and Further Reading
3. Bayesian Foundations
Bayesian Foundations
Bayesian Statistics
The Features of a Bayesian Data Analysis
References and Further Reading
Bayesian Model Specification
Using Probability Distributions to Quantify Uncertainty
Posterior as Compromise Between Likelihood and Prior
Standard Probability Distributions
Poisson Distribution
Binomial Distribution
Normal Distribution
Half-t Distribution
Exchangeability
Partial Exchangeability
Exchangeability within Groups
Exchangeable Residuals
Exchangeable Increments
Pooling Information
Hierarchy
Incorporating External Information
Priors
Covariates
Embedding the Model in a Larger Model
References and Further Reading
Bayesian Inference and Model Checking
Computation
Summarizing the Posterior Distribution
Summary Measures
Calculating Posterior Summaries
Derived Distributions
Posterior Distribution for Derived Quantities
Posterior Predictive Distribution
Missing Data
Forecasting
Model Checking
Responsible Modellers Check and Revise their Models
Held back Data
Replicate Data
*Simulation and Calibration
References and Further Reading
- Inferring Arrays from Reliable Data
Inferring Demographic Arrays from Reliable Data
Summary of the Framework of Part III
Applications
References and Further Reading
Infant Mortality in Sweden
The Infant Mortality Rate
Modelling Infant Mortality Rates in Swedish Counties
Model
Likelihood
Model for Underlying Infant Mortality Rates
Prior for the Region Effect
Prior for Time Effect
Prior for Intercept
Prior for Standard Deviation
Summary
Results
Infant Mortality Rates
Intercept, Region Effects, and Time Effects
Prior for Time Effect
Standard Deviations
Model Checking
Model Predictions versus Direct Estimates
Regional Variation in Slopes
Summarizing Results via Probabilities
Forecasting
Constructing the Forecasts
Results: Exploding Credible Intervals for Forecasting
A Partial Solution
References and Further Reading
Life Expectancy in Portugal
Mortality Rates
The Log Function
Life Expectancy
Age, Sex, and Time Effects
Interactions
Models
Likelihood
Model for Mortality Rates
Prior for Age Effect
Prior for Time Effect
Prior for Age-Time Interaction
Prior for Sex-Time Interaction
Priors for Other Terms
Summary
Model Choice Using Heldback Data
Estimating and Forecasting with the Baseline and Alternative
Models
Comparing the Forecasts with the Heldback Data
Results
Forecasting of Life Expectancy for -
*Obtaining Forecasts of Life Expectancy
References and Further Reading
Health Expenditure in the Netherlands
A Simple Expenditure Projection
Expenditure Projections for the Netherlands
A Statistical Model for Per Capita Expenditures
Model Checking via Replicate Data
Revised Expenditure Projections
Forecasting Policy Outcomes
References and Further Reading
- Inferring Arrays from Unreliable Data
Inferring Demographic Arrays from Unreliable Data
Summary of the Framework
Data Models
Applications
References and Further Reading
Internal Migration in Iceland
Internal Migration in Iceland
Continentalization by Random Rounding to Base Three
Overview of Model
System Model
Data Model
Estimation
Results for Unconfidentialized Migration Counts
Results for Migration Rates
Forecasting
References and Further Reading
Fertility in Cambodia
Data
Overview of Model
System Model
Data Models
Census
Demographic and Health Survey
Results
Revised Model
Final Model
References and Further Reading
- Inferring Accounts
Inferring Demographic Accounts
Summary of Our Approach
Applications
The Role of Demographic Accounts in Official Statistical Systems
References and Further Reading
Population in New Zealand
Input Data for the National Demographic Account
Model for National Demographic Account
Overview
Account
System Models
Data Models
Estimation
Results for the National Demographic Account
Sensitivity Tests for the National Demographic Account
Input Data for the Regional Demographic Account
Model for the Regional Demographic Account
System Models
xii Contents
Data Models
Results for the Regional Demographic Account
References and Further Reading
Population in China
Input Data
Model
Overview
Account
System Models
Data Models
Estimation and Forecasting
Results
References and Further Reading
Conclusion
About the Author :
John Bryant is a senior researcher at Statistics New Zealand. He has previously worked at the New Zealand Treasury, and at universities in New Zealand and Thailand. He has consulted for many international organizations, including UNICEF, the FAO, and the World Bank. His research interests include applied demography, data science, and Bayesian statistics.
Junni L. Zhang is an associate professor of statistics at Guanghua School of Management, Peking University. Her research interests include Bayesian statistics, text mining, and causal inference. She has extensive experience teaching undergraduate, graduate, MBA and executive courses, and is the author of Data Mining and Its Applications (in Chinese).
