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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
List of contents
Preface1 Introduction I Demographic Foundations 2 Demographic Foundations 3 Demographic Individuals 4 Demographic Arrays 5 Demographic Accounts 6 Demographic Data II Bayesian Foundations 7 Bayesian Foundations 8 Bayesian Model Specification 9 Bayesian Inference and Model Checking III Inferring Arrays from Reliable Data 10 Inferring Demographic Arrays from Reliable Data 11 Infant Mortality in Sweden 12 Life Expectancy in Portugal 13 Health Expenditure in the Netherlands IV Inferring Arrays from Unreliable Data 14 Inferring Demographic Arrays from Unreliable Data 15 Internal Migration in Iceland 16 Fertility in Cambodia V Inferring Accounts 17 Inferring Demographic Accounts 18 Population in New Zealand 19 Population in China 20 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).
Summary
Demographic estimation and forecasting is an important practical problem. Population projections are used to guide billions of dollars of expenditure on things such as roads, housing, shopping complexes, and hospitals. Policy evaluations require detailed estimates of populations at risk, or of demographic outcomes such as mortality.
