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Small Area Estimation and Microsimulation Modeling





ISBN 9780367261269
Published March 22, 2019 by Chapman and Hall/CRC
521 Pages 70 B/W Illustrations

 
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Book Description

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.

Table of Contents

Introduction
Introduction
Main Aims of the Book
Guide for the Reader
Concluding remarks

Small Area Estimation
Introduction
Small area estimation
Approaches to small area estimation
Direct estimation
Concluding remarks

Indirect Estimation: Statistical Approaches
Introduction
Implicit models approach
Explicit models approach
Methods for estimating explicit models
A comparison of three methods
Concluding remarks

Indirect Estimation: Geographic Approaches
Introduction
Microsimulation modelling
Methodologies in microsimulation modelling technology
Combinatorial optimisation reweighting approach
Reweighting: The GREGWT approach
A comparison between GREGWT and CO 87
Concluding remarks

Bayesian Prediction-Based Microdata Simulation
Introduction
The basic steps
The Bayesian prediction theory
The multivariate model
The prior and posterior distributions
The linkage model
Prediction for modelling unobserved population units
Concluding remarks

Microsimulation Modelling Technology for Small Area Estimation
Introduction
Data sources and issues
MMT-Based Model Specification
Housing stress
Small area estimation of housing stress
Concluding remarks

Applications of the Methodologies
Introduction
Results of the model: A general view
Estimation of households in housing stress by spatial scales
Small area estimates: Number of households in housing stress
Small area estimates: Percentage of households in housing stress
Concluding remarks

Analysis of Small Area Estimates in Capital Cities
Introduction
Sydney
Melbourne
Brisbane
Perth
Adelaide
Canberra
Hobart
Darwin
Concluding remarks

Validation and Measure of Statistical Reliability
Introduction
Some validation methods in the literature
New approaches to validating housing stress estimation
Measure of statistical reliability of the MMT estimates
Concluding remarks

Conclusions and Computing Codes
Introduction
Summary of major findings
Limitations
Areas offurther studies
Computing codes and programming
Concluding remarks

References

Appendices

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Author(s)

Biography

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. Rahman’s 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.

Reviews

"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

"I enjoyed reading this comprehensively written book. I recommend this book to sociologists, economists, geographers, statistics and computing professionals."
-Ramalingam Shanmugam, in Journal of Statistical Computation and Simulation, June 2019