1st Edition

Small Area Estimation and Microsimulation Modeling

By Azizur Rahman, Ann Harding Copyright 2017
    521 Pages 70 B/W Illustrations
    by Chapman & Hall

    522 Pages 70 B/W Illustrations
    by Chapman & Hall

    521 Pages 70 B/W Illustrations
    by Chapman & Hall

    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.


    • 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.

    Main Aims of the Book
    Guide for the Reader
    Concluding remarks

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

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

    Indirect Estimation: Geographic Approaches
    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
    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
    Data sources and issues
    MMT-Based Model Specification
    Housing stress
    Small area estimation of housing stress
    Concluding remarks

    Applications of the Methodologies
    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
    Concluding remarks

    Validation and Measure of Statistical Reliability
    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
    Summary of major findings
    Areas offurther studies
    Computing codes and programming
    Concluding remarks




    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.

    "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