Buch, Englisch, 288 Seiten, Format (B × H): 202 mm x 253 mm, Gewicht: 894 g
ISBN: 978-0-691-13095-8
Verlag: Princeton University Press
Demographic Forecasting introduces new statistical tools that can greatly improve forecasts of population death rates. Mortality forecasting is used in a wide variety of academic fields, and for policymaking in global health, social security and retirement planning, and other areas. Federico Girosi and Gary King provide an innovative framework for forecasting age-sex-country-cause-specific variables that makes it possible to incorporate more information than standard approaches. These new methods more generally make it possible to include different explanatory variables in a time-series regression for each cross section while still borrowing strength from one regression to improve the estimation of all. The authors show that many existing Bayesian models with explanatory variables use prior densities that incorrectly formalize prior knowledge, and they show how to avoid these problems. They also explain how to incorporate a great deal of demographic knowledge into models with many fewer adjustable parameters than classic Bayesian approaches, and develop models with Bayesian priors in the presence of partial prior ignorance. By showing how to include more information in statistical models, Demographic Forecasting carries broad statistical implications for social scientists, statisticians, demographers, public-health experts, policymakers, and industry analysts.Introduces methods to improve forecasts of mortality rates and similar variables Provides innovative tools for more effective statistical modeling Makes available free open-source software and replication data Includes full-color graphics, a complete glossary of symbols, a self-contained math refresher, and more
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziale Gruppen/Soziale Themen Sozialprognosen
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Demographie, Demoskopie
Weitere Infos & Material
List of Figures xi
List of Tables xiii
Preface xv
Acknowledgments xvii
Chapter 1: Qualitative Overview 1
1.1 Introduction 1
1.2 Forecasting Mortality 3
1.2.1 The Data 3
1.2.2 The Patterns 5
1.2.3 Scientific versus Optimistic Forecasting Goals 8
1.3 Statistical Modeling 11
1.4 Implications for the Bayesian Modeling Literature 15
1.5 Incorporating Area Studies in Cross-National Comparative Research 16
1.6 Summary 18
Part I Existing Methods for Forecasting Mortality 19
Chapter 2: Methods without Covariates 21
2.1 Patterns in Mortality Age Profiles 22
2.2 A Unified Statistical Framework 24
2.3 Population Extrapolation Approaches 25
2.4 Parametric Approaches 26
2.5 A Nonparametric Approach: Principal Components 28
2.5.1 Introduction 28
2.5.2 Estimation 32
2.6 The Lee-Carter Approach 34
2.6.1 The Model 34
2.6.2 Estimation 36
2.6.3 Forecasting 36
2.6.4 Properties 38
2.7 Summary 42
Chapter 3: Methods with Covariates 43
3.1 Equation-by-Equation Maximum Likelihood 43
3.1.1 Poisson Regression 43
3.1.2 Least Squares 44
3.1.3 Computing Forecasts 46
3.1.4 Summary Evaluation 47
3.2 Time-Series, Cross-Sectional Pooling 48
3.2.1 The Model 48
3.2.2 Postestimation Intercept Correction 49
3.2.3 Summary Evaluation 49
3.3 Partially Pooling Cross Sections via Disturbance Correlations 50
3.4 Cause-Specific Methods with Microlevel Information 51
3.4.1 Direct Decomposition Methods 51
Modeling 51
3.4.2 Microsimulation Methods 52
3.4.3 Interpretation 53
3.5 Summary 53
Part II Statistical Modeling 55
Chapter 4: The Model 57
4.1 Overview 57
4.2 Priors on Coefficients 59
4.3 Problems with Priors on Coefficients 60
4.3.1 Little Direct Prior Knowledge Exists about Coefficients 61
4.3.2 Normalization Factors Cannot Be Estimated 62
4.3.3 We Know about the Dependent Variable, Not the Coefficients 64
4.3.4 Difficulties with Incomparable Covariates 65
4.4 Priors on the Expected Value of the Dependent Variable 65
4.4.1 Step 1: Specify a Prior for the Dependent Variable 66
4.4.2 Step 2: Translate to a Prior on the Coefficients 67
4.4.3 Interpretation 68
4.5 A Basic Prior for Smoothing over Age Groups 69
4.5.1 Step 1: A Prior for ? 69
4.5.2 Step 2: From the Prior on ? to the Prior on ? 71
4.5.3 Interpretation 71
4.6 Concluding Remark 73
Chapter 5: Priors over Grouped Continuous Variables 74
5.1 Definition and Analysis of Prior Indifference 74
5.1.1 A Simple Special Case 76
5.1.2 General Expressions for Prior Indifference 76
5.1.3 Interpretation 77
5.2 Step 1: A Prior for ? 80
5.2.1 Measuring Smoothness 81
5.2.2 Varying the Degree of Smoothness over Age Groups 83
5.2.3 Null Space and Prior Indifference 83
5.2.4 Nonzero Mean Smoothness Functional 85
5.2.5 Discretizing: From Age to Age Groups 85
5.2.6 Interpretation 86
5.3 Step 2: From the Prior on ? to the Prior on ? 92
5.3.1 Analysis 92
5.3.2 Interpretation 92
Chapter 6: Model Selection 94
6.1 Choosing the Smoothness Functional 94
6.2 Choosing a Prior for the Smoothing Parameter 97
6.2.1 Smoothness Parameter for a Nonparametric Prior 98
6.2.2 Smoothness Parameter for the Prior over the Coefficients 100
6.3 Choosing Where to Smooth 104
6.4 Choosing Covariates 108
6.4.1 Size of the Null Space 109
6.4.2 Content of the Null Space 110
6.5 Choosing a Likelihood and Variance Function 112
6.5.1 Deriving the Normal Specification 112
6.5.2 Accuracy of the Log-Normal Approximation to the Poisson 114
6.5.3 Variance Specification 120
Chapter 7: Adding Priors over Time and Space 124
7.1 Smoothing over Time 124
7.1.1 Prior Indifference and the Null Space 125
7.2 Smoothing over Countries 127
7.2.1 Null Space and Prior Indifference 128
7.2.2 Interpretation 130
7.3 Smoothing Simultaneously over Age, Country, and Time 131
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