Bayesian Demographic Estimation and Forecasting: (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

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:
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).