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E-raamat: Small Area Estimation and Microsimulation Modeling

(Charles Sturt University, Wagga Wagga, NSW, Australia), (University of Canberra, Australia)
  • Formaat: 521 pages
  • Ilmumisaeg: 30-Nov-2016
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781482260731
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Small Area Estimation and Microsimulation ModelingSuurem pilt
  • Formaat: 521 pages
  • Ilmumisaeg: 30-Nov-2016
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781482260731
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Small Area Estimation and Microsimulation Modeling is the first practical handbook that comprehensively presents modern statistical SAE methods in the framework of ultramodern spatial microsimulation modeling while providing the novel approach of creating synthetic spatial microdata. Along with describing the necessary theories and their advantages and limitations, the authors illustrate the practical application of the techniques to a large number of substantive problems, including how to build up models, organize and link data, create synthetic microdata, conduct analyses, yield informative tables and graphs, and evaluate how the findings effectively support the decision making processes in government and non-government organizations.

Features











Covers both theoretical and applied aspects for real-world comparative research and regional statistics production





Thoroughly explains how microsimulation modeling technology can be constructed using available datasets for reliable small area statistics





Provides SAS codes that allow readers to utilize these latest technologies in their own work.

This book is designed for advanced graduate students, academics, professionals and applied practitioners who are generally interested in small area estimation and/or microsimulation modeling and dealing with vital issues in social and behavioural sciences, applied economics and policy analysis, government and/or social statistics, health sciences, business, psychology, environmental and agriculture modeling, computational statistics and data simulation, spatial statistics, transport and urban planning, and geospatial modeling.

Dr Azizur Rahman is a Senior Lecturer in Statistics and convenor of the Graduate Program in Applied Statistics at the Charles Sturt University, and an Adjunct Associate Professor of Public Health and Biostatistics at the University of Canberra. His research encompasses small area estimation, applied economics, microsimulation modeling, Bayesian inference and public health. He has more than 60 scholarly publications including two books. Dr. Rahmans research is funded by the Australian Federal and State Governments, and he serves on a range of editorial boards including the International Journal of Microsimulation (IJM).

Professor Ann Harding, AO is an Emeritus Professor of Applied Economics and Social Policy at the National Centre for Social and Economic Modelling (NATSEM) of the University of Canberra. She was the founder and inaugural Director of this world class Research Centre for more than sixteen years, and also a co-founder of the International Microsimulation Association (IMA) and served as the inaugural elected president of IMA from 2004 to 2011. She is a fellow of the Academy of the Social Sciences in Australia. She has more than 300 publications including several books in microsimulation modeling.

Arvustused

The authors describe theories and provide practical applications in economics, behavioural sciences, health sciences, business, psychology, environmental sciences, transportation problems, urban planning, and computational issues in this book. Some unique features of this book are the following. The historical quotes from early time as far as fifth century are amusing. The references related to SAE are not only thorough but also up to date. The guidance for the readers does ease the readability. I enjoyed reading this comprehensively written book. I recommend this book to sociologists, economists, geographers, statistics and computing professionals. Ramalingam Shanmugam, in the Journal of Statistical Computation and Simulation, June 2019

"The book aims at introducing modern statistical small area estimation methodologies into the framework of spatial microsimulation modelling for a comprehensive presentation, providing a novel approach with much potential in comparative social research and regional statistics production. In my opinion, the strongest methodological developments are in the techniques of generating synthetic spatial microdata at small area levels. This book will be attractive for students, in economics, social sciences and statistics in particular. The increasing use of both SAE and microsimulation methods in different areas of society, such as social planning by government institutions and official or public statistics production by national and international statistical agencies. Finally, I want to congratulate the authors for writing a nice and well readable book on a quite complicated topic." Prof. Risto Lehtonen, University of Helsinki

". . .an interesting read for both beginning and more experienced microsimulation modellers. The two authors are well known within the microsimulation community. In this book, they share their experiences and insights into both the more theoretical and empirical aspects of microsimulation modelling. Across disciplines, there are several approaches towards the simulation or projection of small area statistics. However, since these different disciplines make use of different terminologies, there is less cross-pollination than expected (or hoped for). The aim of this book is to show and explain different approaches of small area estimation that are used in different research fields. The book gives an extensive theoretical and empirical overview of different microsimulation techniques and can be of relevance to researchers who want to expand their knowledges on ways to estimate small area characteristics." ~International Journal of Microsimulation

"The authors begin with a detailed classification tree of small area estimation techniques. The text then proceeds to review and describe these techniques. A familiarity with regression techniques and survey methods is assumed throughout. The text then proceeds to present some new small area estimation techniques, validation methods, and a detailed worked example. The appendices provide further details of the worked example and SAS code for the generalized regression weighting tool (GREGWT) method." ~Douglas Dover, International Society for Clinical Biostatistics The authors describe theories and provide practical applications in economics, behavioural sciences, health sciences, business, psychology, environmental sciences, transportation problems, urban planning, and computational issues in this book. Some unique features of this book are the following. The historical quotes from early time as far as fifth century are amusing. The references related to SAE are not only thorough but also up to date. The guidance for the readers does ease the readability. I enjoyed reading this comprehensively written book. I recommend this book to sociologists, economists, geographers, statistics and computing professionals. Ramalingam Shanmugam, in the Journal of Statistical Computation and Simulation, June 2019

"The book aims at introducing modern statistical small area estimation methodologies into the framework of spatial microsimulation modelling for a comprehensive presentation, providing a novel approach with much potential in comparative social research and regional statistics production. In my opinion, the strongest methodological developments are in the techniques of generating synthetic spatial microdata at small area levels. This book will be attractive for students, in economics, social sciences and statistics in particular. The increasing use of both SAE and microsimulation methods in different areas of society, such as social planning by government institutions and official or public statistics production by national and international statistical agencies. Finally, I want to congratulate the authors for writing a nice and well readable book on a quite complicated topic." ~Prof. Risto Lehtonen, University of Helsinki

". . .an interesting read for both beginning and more experienced microsimulation modellers. The two authors are well known within the microsimulation community. In this book, they share their experiences and insights into both the more theoretical and empirical aspects of microsimulation modelling. Across disciplines, there are several approaches towards the simulation or projection of small area statistics. However, since these different disciplines make use of different terminologies, there is less cross-pollination than expected (or hoped for). The aim of this book is to show and explain different approaches of small area estimation that are used in different research fields. The book gives an extensive theoretical and empirical overview of different microsimulation techniques and can be of relevance to researchers who want to expand their knowledges on ways to estimate small area characteristics." ~International Journal of Microsimulation

"The authors begin with a detailed classification tree of small area estimation techniques. The text then proceeds to review and describe these techniques. A familiarity with regression techniques and survey methods is assumed throughout. The text then proceeds to present some new small area estimation techniques, validation methods, and a detailed worked example. The appendices provide further details of the worked example and SAS code for the generalized regression weighting tool (GREGWT) method." ~Douglas Dover, International Society for Clinical Biostatistics

List of Figures
xiii
List of Tables
xvii
Preface xxi
Acknowledgments xxiii
List of Abbreviations
xxv
1 Introduction
1(10)
1.1 Introduction
1(4)
1.2 Main Aims of the Book
5(1)
1.3 Guide for the Reader
6(4)
1.4 Concluding Remarks
10(1)
2 Small Area Estimation
11(16)
2.1 Introduction
11(1)
2.2 Small Area Estimation
11(4)
2.2.1 Concept of Small Area
12(1)
2.2.2 Advantages of SAE
12(1)
2.2.3 Why SAE Techniques?
13(1)
2.2.4 Applications of SAE
13(2)
2.3 Approaches to SAE
15(2)
2.4 Direct Estimation
17(7)
2.4.1 H-T Estimator
17(1)
2.4.2 Generalized Regression Estimator
18(1)
2.4.3 Modified Direct Estimator
18(1)
2.4.4 Design-Based Model-Assisted Estimators
19(3)
2.4.5 A Comparison of Direct Estimators
22(2)
2.5 Concluding Remarks
24(3)
3 Indirect Estimation: Statistical Approaches
27(24)
3.1 Introduction
27(1)
3.2 Implicit Models Approach
28(5)
3.2.1 Synthetic Estimation
28(1)
3.2.2 Composite Estimation
29(1)
3.2.3 Demographic Estimation
30(2)
3.2.4 Comparison of Various Implicit Models-Based Indirect Estimation
32(1)
3.3 Explicit Models Approach
33(9)
3.3.1 Basic Area Level Model
33(2)
3.3.2 Basic Unit Level Model
35(1)
3.3.3 Generalized Linear Mixed Model
36(5)
3.3.4 Comparison of Various Explicit Models-Based Indirect Estimation
41(1)
3.4 Methods for Estimating Explicit Models
42(5)
3.4.1 EBLUP Approach
42(1)
3.4.2 EB Approach
43(2)
3.4.3 HB Approach
45(2)
3.4 A Comparison of Three Methods
47(2)
3.5 Concluding Remarks
49(2)
4 Indirect Estimation: Geographic Approaches
51(50)
4.1 Introduction
51(1)
4.2 Microsimulation Modeling
52(17)
4.2.1 Process of Microsimulation
52(2)
4.2.2 Types of Microsimulation Models
54(1)
4.2.2.1 Static Microsimulation
55(2)
4.2.2.2 Dynamic Microsimulation
57(2)
4.2.2.3 Spatial Microsimulation
59(9)
4.2.3 Advantages of Microsimulation Modeling
68(1)
4.3 Methodologies in Microsimulation Modeling Technology
69(12)
4.3.1 Techniques for Creating Spatial Microdata
69(1)
4.3.2 Statistical Data Matching or Fusion
70(2)
4.3.3 Iterative Proportional Fitting
72(2)
4.3.4 Repeated Weighting Method
74(6)
4.3.5 Reweighting
80(1)
4.4 CO Reweighting Approach
81(6)
4.4.1 Simulated Annealing Method in CO
83(2)
4.4.2 Illustration of CO Process for Hypothetical Data
85(2)
4.5 Reweighting: The GREGWT Approach
87(10)
4.5.1 Theoretical Setting
88(2)
4.5.2 How Does GREGWT Generate New Weights?
90(1)
4.5.3 Explicit Numerical Solution for Hypothetical Data
91(6)
4.6 Comparison between GREGWT and CO
97(2)
4.7 Concluding Remarks
99(2)
5 Bayesian Prediction-Based Microdata Simulation
101(18)
5.1 Introduction
101(1)
5.2 Basic Steps
102(1)
5.3 Bayesian Prediction Theory
103(1)
5.4 Multivariate Model
103(3)
5.5 Prior and Posterior Distributions
106(3)
5.6 The Linkage Model
109(1)
5.7 Prediction for Modeling Unobserved Population Units
110(7)
5.8 Concluding Remarks
117(2)
6 Microsimulation Modeling Technology for Small Area Estimation
119(28)
6.1 Introduction
119(1)
6.2 Data Sources and Issues
120(4)
6.2.1 Census Data
120(2)
6.2.2 Survey Data Sets
122(2)
6.3 Microsimulation Modeling Technology-Based Model Specification
124(12)
6.3.1 Model Inputs
125(1)
6.3.1.1 General Model File
126(1)
6.3.1.2 Unit Record Data Files
126(2)
6.3.1.3 Benchmark Files
128(1)
6.3.1.4 Auxiliary Data Files
128(4)
6.3.1.5 GREGWT File
132(1)
6.3.2 Generating Small Area Synthetic Weights
132(2)
6.3.3 Model Outputs
134(2)
6.4 Housing Stress
136(5)
6.4.1 Definition
136(1)
6.4.2 Measures of Housing Stress
136(2)
6.4.3 Comparison of Various Measures
138(3)
6.5 Small Area Estimation of Housing Stress
141(3)
6.5.1 Inputs at the Second-Stage Model
141(1)
6.5.1.1 Consumer Price Index File
141(1)
6.5.2 Model Execution Process
142(1)
6.5.3 Final Model Outputs
143(1)
6.6 Concluding Remarks
144(3)
7 Applications of the Methodologies
147(34)
7.1 Introduction
147(1)
7.2 Results of the Model: A General View
147(8)
7.2.1 Model Accuracy Report
147(1)
7.2.2 Scenarios of Housing Stress under Various Measures
148(2)
7.2.3 Distribution of Housing Stress Estimation
150(1)
7.2.4 Lorenz Curve for Housing Stress Estimates
151(1)
7.2.5 Proportional Cumulative Frequency Graph and Index of Dissimilarity
152(2)
7.2.6 Scenarios of Households and Housing Stress by Tenures
154(1)
7.3 Estimation of Households in Housing Stress by Spatial Scales
155(7)
7.3.1 Results for Different States
155(2)
7.3.2 Results for Various Statistical Divisions
157(2)
7.3.3 Results for Various Statistical Subdivisions
159(3)
7.4 Small Area Estimates: Number of Households in Housing Stress
162(9)
7.4.1 Estimated Numbers of Overall Households in Housing Stress
165(1)
7.4.2 Estimated Numbers of Buyer Households in Housing Stress
166(1)
7.4.3 Estimated Numbers of Public Renter Households in Housing Stress
167(2)
7.4.4 Estimated Numbers of Private Renter Households in Housing Stress
169(1)
7.4.5 Estimated Numbers of Total Renter Households in Housing Stress
170(1)
7.5 Small Area Estimates: Percentage of Households in Housing Stress
171(6)
7.5.1 Percentage Estimates of Housing Stress for Overall Households
171(3)
7.5.2 Percentage Estimates of Housing Stress for Buyer Households
174(1)
7.5.3 Percentage Estimates of Housing Stress for Public Renter Households
175(1)
7.5.4 Percentage Estimates of Housing Stress for Private Renter Households
176(1)
7.5.5 Percentage Estimates of Housing Stress for Total Renter Households
177(1)
7.6 Concluding Remarks
177(4)
8 Analysis of Small Area Estimates in Capital Cities
181(40)
8.1 Introduction
181(5)
8.1.1 Scenarios of the Results for Major Capital Cities
182(1)
8.1.2 Trends in Housing Stress for Some Major Cities
183(1)
8.1.3 Mapping the Estimates at SLA Levels within Major Cities
184(2)
8.2 Sydney
186(4)
8.2.1 Housing Stress Estimates for Overall Households
186(2)
8.2.2 Small Area Estimation by Households' Tenure Types...
188(1)
8.2.2.1 Estimates for Buyers
188(1)
8.2.2.2 Estimates for Public Renters
188(1)
8.2.2.3 Estimates for Private Renters
189(1)
8.2.2.4 Estimates for the Total Renters
189(1)
8.3 Melbourne
190(5)
8.3.1 Housing Stress Estimates for Overall Households
191(1)
8.3.2 Small Area Estimation by Households' Tenure Types
192(1)
8.3.2.1 Estimates for Buyers
192(1)
8.3.2.2 Estimates for Public Renters
192(1)
8.3.2.3 Estimates for Private Renters
193(1)
8.3.2.4 Estimates for Total Renters
194(1)
8.4 Brisbane
195(4)
8.4.1 Housing Stress Estimates for Overall Households
196(1)
8.4.2 Small Area Estimation by Households' Tenure Types
197(1)
8.4.2.1 Estimates for Buyers
197(1)
8.4.2.2 Estimates for Public Renters
197(1)
8.4.2.3 Estimates for Private Renters
198(1)
8.4.2.4 Estimates for the Total Renters
198(1)
8.5 Perth
199(5)
8.5.1 Housing Stress Estimates for Overall Households
200(1)
8.5.2 Small Area Estimation by Households' Tenure Types
200(1)
8.5.2.1 Estimates for Buyers
200(1)
8.5.2.2 Estimates for Public Renters
201(1)
8.5.2.3 Estimates for Private Renters
202(1)
8.5.2.4 Estimates for the Total Renters
203(1)
8.6 Adelaide
204(4)
8.6.1 Housing Stress Estimates for Overall Households
204(1)
8.6.2 Small Area Estimation by Households' Tenure Types
205(1)
8.6.2.1 Estimates for Buyers
205(1)
8.6.2.2 Estimates for Public Renters
206(1)
8.6.2.3 Estimates for Private Renters
207(1)
8.6.2.4 Estimates for the Total Renters
207(1)
8.7 Canberra
208(4)
8.7.1 Housing Stress Estimates for Overall Households
209(1)
8.7.2 Small Area Estimation by Households' Tenure Types
210(1)
8.7.2.1 Estimates for Buyers
210(1)
8.7.2.2 Estimates for Public Renters
210(1)
8.7.2.3 Estimates for Private Renters
211(1)
8.7.2.4 Estimates for the Total Renters
211(1)
8.8 Hobart
212(3)
8.8.1 Housing Stress Estimates for Overall Households
212(1)
8.8.2 Small Area Estimation by Households' Tenure Types
213(1)
8.8.2.1 Estimates for Buyers
213(1)
8.8.2.2 Estimates for Public Renters
213(1)
8.8.2.3 Estimates for Private Renters
214(1)
8.8.2.4 Estimates for the Total Renters
214(1)
8.9 Darwin
215(4)
8.9.1 Housing Stress Estimates for Overall Households
216(1)
8.9.2 Small Area Estimation by Households' Tenure Types
217(1)
8.9.2.1 Estimates for Buyers
217(1)
8.9.2.2 Estimates for Public Renters
217(1)
8.9.2.3 Estimates for Private Renters
218(1)
8.9.2.4 Estimates for the Total Renters
218(1)
8.10 Concluding Remarks
219(2)
9 Validation and Measure of Statistical Reliability
221(28)
9.1 Introduction
221(1)
9.2 Some Validation Methods in the Literature
222(3)
9.3 New Approaches to Validating Housing Stress Estimation
225(14)
9.3.1 Statistical Significance Test of the MMT Estimates
225(3)
9.3.2 Results of the Statistical Significance Test
228(7)
9.3.3 Absolute Standardized Residual Estimate Analysis
235(1)
9.3.4 Results from the ASRE Analysis
236(3)
9.4 Measure of Statistical Reliability of the MMT Estimates
239(7)
9.4.1 Confidence Interval Estimation
240(2)
9.4.2 Results from the Estimates of Confidence Intervals
242(4)
9.5 Concluding Remarks
246(3)
10 Conclusions and Computing Codes
249(46)
10.1 Introduction
249(1)
10.2 Summary of Major Findings
249(8)
10.3 Limitations
257(2)
10.4 Areas of Further Studies
259(1)
10.5 Computing Codes and Programming
260(34)
10.5.1 The General Model File Codes
260(9)
10.5.2 SAS Programming for Reweighting Algorithms
269(14)
10.5.3 The Second-Stage Program File Codes
283(11)
10.6 Concluding Remarks
294(1)
References 295(26)
Appendix A The Newton-Raphson Iteration Method 321(4)
Appendix B Topics Index of the 2005-2006 Survey of Income and Housing: CURFs 325(16)
Appendix C Tables of the Housing Stress for 50 SLAs with the Highest Numbers and Percentages Estimates 341(20)
Appendix D Distribution of SLAs, Households, and Housing Stress by SSDs in Eight Major Capital Cities 361(4)
Appendix E Spatial Analyses by Households Tenure Types for the Eight Capital Cities 365(76)
Appendix F SAS Programming for the Reweighting Algorithms from Parts 2 to 10 441(44)
Index 485
Associate Professor Azizur Rahman, PhD, is a statistician and data scientist with expertise in both developing and applying novel methodologies, models and technologies. He is the Leader of Statistics and Data Mining Research Group at the Charles Sturt University (CSU), and able to assist in understanding multi-disciplinary research issues within various fields including how to understand the individual activities which occur within very complex scientific, behavioural, socio-economic and ecological systems. His research encompasses issues in simple to multi-facet analyses in various fields ranging from the statistical sciences to the law and legal studies. He has more than 100 scholarly publications including a few books. Prof. Rahmans research is funded by the Australian Federal and State Governments, and he serves on a range of editorial boards including the International Journal of Microsimulation (IJM) and Sustaining Regions. He obtained several awards including the SOCM Research Excellence Award 2018 and the CSU-RED Achievement Award 2019.

Professor Ann Harding, AO, is an Emeritus Professor of Applied Economics and Social Policy at the National Centre for Social and Economic Modelling (NATSEM) of the University of Canberra. She was the founder and inaugural Director of this world class Research Centre for more than sixteen years, and also a co-founder of the International Microsimulation Association (IMA) and served as the inaugural elected president of IMA from 2004 to 2011. She is a fellow of the Academy of the Social Sciences in Australia. She has more than 300 publications including several books in microsimulation modeling.
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