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E-raamat: Bayesian Demographic Estimation and Forecasting

, (Peking University, Haidian District, Beijing, People's Republic of China)
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Bayesian Demographic Estimation and ForecastingSuurem pilt
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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 forecastersas 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
Preface xi
1 Introduction
1(12)
1.1 Example: Mortality Rates for Maori
2(3)
1.2 Our Approach to Demographic Estimation and Forecasting
5(4)
1.3 Outline of the Rest of the Book
9(2)
1.4 References and Further Reading
11(2)
I Demographic Foundations
13(48)
2 Demographic Foundations
15(2)
2.1 References and Further Reading
16(1)
3 Demographic Individuals
17(8)
3.1 Attributes
17(1)
3.2 Events
18(1)
3.3 Lexis Diagram
19(1)
3.4 Twelve Fictitious Individuals
20(4)
3.5 References and Further Reading
24(1)
4 Demographic Arrays
25(18)
4.1 Population Counts
25(2)
4.2 Death Counts
27(1)
4.3 Movements
28(2)
4.4 Alternative Representations of Changing Statuses
30(2)
4.5 Non-Demographic Events
32(1)
4.6 Exposure
33(1)
4.7 Age, Period, and Cohort
34(2)
4.8 Rates, Proportions, Means, and Ratios
36(2)
4.9 Super-Population and Finite-Population Quantities
38(2)
4.10 Collapsing Dimensions
40(2)
4.11 References and Further Reading
42(1)
5 Demographic Accounts
43(12)
5.1 Demographic Systems
43(1)
5.2 Demographic Accounts
44(1)
5.3 Account with No Region and No Age
45(1)
5.4 Account with Region and No Age
46(2)
5.5 Account with Age and No Region
48(3)
5.6 Movements Accounts and Transitions Accounts*
51(1)
5.7 Mathematical Description of Accounting Identities*
52(2)
5.8 References and Further Reading
54(1)
6 Demographic Data
55(6)
6.1 Traditional Data Sources
55(1)
6.2 New Data Sources
56(2)
6.3 Data Quality and Model Choice
58(1)
6.4 References and Further Reading
59(2)
II Bayesian Foundations
61(54)
7 Bayesian Foundations
63(6)
7.1 Bayesian Statistics
63(1)
7.2 Features of a Bayesian Data Analysis
64(3)
7.3 References and Further Reading
67(2)
8 Bayesian Model Specification
69(24)
8.1 Using Probability Distributions to Quantify Uncertainty
69(2)
8.2 Posterior as a Compromise between Likelihood and Prior
71(2)
8.3 Standard Probability Distributions
73(5)
8.3.1 Poisson Distribution
73(2)
8.3.2 Binomial Distribution
75(1)
8.3.3 Normal Distribution
75(1)
8.3.4 Half-t Distribution
76(2)
8.4 Exchangeability
78(2)
8.5 Partial Exchangeability
80(3)
8.5.1 Exchangeability within Groups
80(1)
8.5.2 Exchangeable Residuals
80(1)
8.5.3 Exchangeable Increments
81(2)
8.6 Pooling Information
83(5)
8.7 Hierarchy
88(1)
8.8 Incorporating External Information
89(3)
8.8.1 Priors
90(1)
8.8.2 Covariates
91(1)
8.8.3 Embedding the Model in a Larger Model
91(1)
8.9 References and Further Reading
92(1)
9 Bayesian Inference and Model Checking
93(22)
9.1 Computation
93(3)
9.2 Summarizing the Posterior Distribution
96(3)
9.2.1 Summary Measures
96(2)
9.2.2 Calculating Posterior Summaries
98(1)
9.3 Derived Distributions
99(3)
9.3.1 Posterior Distribution for Derived Quantities
99(2)
9.3.2 Posterior Predictive Distribution
101(1)
9.4 Missing Data
102(3)
9.5 Forecasting
105(3)
9.6 Model Checking
108(4)
9.6.1 Responsible Modelers Check and Revise their Models
108(1)
9.6.2 Heldback Data
108(1)
9.6.3 Replicate Data
109(3)
9.7 Simulation and Calibration*
112(2)
9.8 References and Further Reading
114(1)
III Inferring Arrays from Reliable Data
115(64)
10 Inferring Demographic Arrays from Reliable Data
117(6)
10.1 Summary of the Framework of Part III
117(3)
10.2 Applications
120(1)
10.3 References and Further Reading
121(2)
11 Infant Mortality in Sweden
123(20)
11.1 Infant Mortality Rate
123(1)
11.2 Infant Mortality Rates in Swedish Counties
124(2)
11.3 Model
126(5)
11.3.1 Likelihood
126(1)
11.3.2 Model for Underlying Infant Mortality Rates
126(2)
11.3.3 Prior for Region Effect
128(1)
11.3.4 Prior for Time Effect
129(1)
11.3.5 Prior for Intercept
130(1)
11.3.6 Prior for Standard Deviation
131(1)
11.3.7 Summary
131(1)
11.4 Results
131(3)
11.4.1 Infant Mortality Rates
131(1)
11.4.2 Intercept, Region Effects, and Time Effects
132(1)
11.4.3 Prior for Time Effect
133(1)
11.4.4 Standard Deviations
134(1)
11.5 Model Checking
134(3)
11.5.1 Model Predictions versus Direct Estimates
135(1)
11.5.2 Regional Variation in Slopes
135(2)
11.6 Summarizing Results via Probabilities
137(1)
11.7 Forecasting
138(3)
11.7.1 Constructing the Forecasts
138(1)
11.7.2 Results: Exploding Credible Intervals for Forecasting
139(1)
11.7.3 A Partial Solution
140(1)
11.8 References and Further Reading
141(2)
12 Life Expectancy in Portugal
143(22)
12.1 Mortality Rates
143(1)
12.2 Log Function
144(1)
12.3 Life Expectancy
145(1)
12.4 Age, Sex, and Time Effects
146(2)
12.5 Interactions
148(3)
12.6 Models
151(5)
12.6.1 Likelihood
151(1)
12.6.2 Model for Mortality Rates
151(1)
12.6.3 Prior for Age Effect
152(1)
12.6.4 Prior for Time Effect
152(1)
12.6.5 Prior for Age-Time Interaction
153(2)
12.6.6 Prior for Sex-Time Interaction
155(1)
12.6.7 Priors for Other Terms
155(1)
12.6.8 Summary
155(1)
12.7 Model Choice Using Heldback Data
156(1)
12.8 Estimating and Forecasting
156(2)
12.9 Comparing the Forecasts with the Heldback Data
158(2)
12.10 Results
160(1)
12.11 Forecasting of Life Expectancy for 2016-2035
161(1)
12.12 Obtaining Forecasts of Life Expectancy*
161(3)
12.13 References and Further Reading
164(1)
13 Health Expenditure in the Netherlands
165(14)
13.1 A Simple Expenditure Projection
165(2)
13.2 Expenditure Projections for the Netherlands
167(4)
13.3 A Statistical Model for Per Capita Expenditures
171(1)
13.4 Model Checking via Replicate Data
172(2)
13.5 Revised Expenditure Projections
174(2)
13.6 Forecasting Policy Outcomes
176(1)
13.7 References and Further Reading
176(3)
IV Inferring Arrays from Unreliable Data
179(38)
14 Inferring Demographic Arrays from Unreliable Data
181(8)
14.1 Summary of the Framework
181(5)
14.2 Data Models
186(1)
14.3 Applications
187(1)
14.4 References and Further Reading
187(2)
15 Internal Migration in Iceland
189(14)
15.1 Internal Migration in Iceland
190(1)
15.2 Confidentialization by Random Rounding to Base Three
190(2)
15.3 Overview of Model
192(2)
15.4 System Model
194(1)
15.5 Data Model
195(1)
115.6 Estimation
196(1)
15.7 Results for Unconfidentialized Migration Counts
196(2)
15.8 Results for Migration Rates
198(2)
15.9 Forecasting
200(1)
15.10 References and Further Reading
201(2)
16 Fertility in Cambodia
203(14)
16.1 Data
203(2)
16.2 Overview of Model
205(1)
16.3 System Model
205(1)
16.4 Data Models
206(2)
16.4.1 2008 Census
206(1)
16.4.2 2010 Demographic and Health Survey
207(1)
16.5 Results
208(2)
16.6 Revised Model
210(2)
16.7 Final Model
212(3)
16.8 References and Further Reading
215(2)
V Inferring Accounts
217(52)
17 Inferring Demographic Accounts
219(8)
17.1 Summary of Our Approach
219(5)
17.2 Applications
224(1)
17.3 Demographic Accounts in Official Statistical Systems
225(1)
17.4 References and Further Reading
226(1)
18 Population in New Zealand
227(24)
18.1 Input Data for the National Demographic Account
228(2)
18.2 Model for National Demographic Account
230(5)
18.2.1 Overview
230(1)
18.2.2 Account
231(1)
18.2.3 System Models
231(1)
18.2.4 Data Models
232(2)
18.2.5 Estimation
234(1)
18.3 Results for the National Demographic Account
235(2)
18.4 Sensitivity Tests for the National Demographic Account
237(2)
18.5 Input Data for the Regional Demographic Account
239(3)
18.6 Model for the Regional Demographic Account
242(4)
18.6.1 System Models
242(2)
18.6.2 Data Models
244(2)
18.7 Results for the Regional Demographic Account
246(3)
18.8 References and Further Reading
249(2)
19 Population in China
251(16)
19.1 Input Data
252(4)
19.2 Model
256(3)
19.2.1 Overview
256(1)
19.2.2 Account
257(1)
19.2.3 System Models
257(1)
19.2.4 Data Models
258(1)
19.2.5 Estimation and Forecasting
258(1)
19.3 Results
259(6)
19.4 References and Further Reading
265(2)
20 Conclusion
267(2)
20.1 References and Further Reading
268(1)
Bibliography 269(8)
Index 277
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).
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