Small Area Estimation and Microsimulation Modeling

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. 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).

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.

Table of Contents

Preface

Introduction

Introduction

Main Aims of the Book

Guide for the Reader

Concluding Remarks

Small Area Estimation

Introduction

Small area estimation

Advantages of small area estimation

Why small area estimation techniques?

Applications of small area estimation

Approaches to small area estimation

Direct estimation

Horvitz-Thomposn (H-T) estimator

Generalized regression (GREG) estimator

Modified direct estimator

Design-based model-assited estimators

A comparison of direct estimators

Concluding remarks

Indirect Estimation: Statistical Approaches

Introduction

Implicit models approach

Synthetic estimaton

Composite estimation

Demographic estimation

Comparison of various implicit models based indirect estimation

Explicit models approach

Basic area level model

Basic unit leve model

General linear mixed model

Comparison of various explicit models based indirect estimation

Methods for estimating explicit models

E-BLUP approach

EB approach

HB approach

A comparison of three methods

Concluding remarks

Indirect Estimation: Geographic Approaches

Introduction

Microsimulation modeling

Process of microsimulation

Types of microsimulation models

Advantages of microsimulation modeling

Methodologies in microsimulation modeling technology

Techniques for creating spatial microdata

Statistical data matching or fusion

Iterative proportional fitting

Repeated weighting method

Reweighting

Combinatorial optimisation reweighing approach

The simulated annealing method in CO

An illustration of CO process for hypothetical data

Reweighting: The GREGWT approach

Theoretical setting

How does GREGWT generate new weights?

Explicit numerical solution for a hypothetical data

A comparison between GREGWT and CO

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 moedling unobserved population units

Concluding remarks

Microsimulation Modelling Technology for Small Area Estimation

Introduction

Data sources and issues

The Census Data

Survey Datasets

Survey Datasets

MMT based Model Specification

Model inputs

Generating small area synthetic weights

Model inputs

Generating small area synthetic weights

Model inputs

Gnerating small area synthetic weights

Model outputs

Housing stress

Definition

Measures of housing stress

A comparison of various measures

Small area estimation of housing stress

Inputs at the second stae model

Final model outputs

Concluding remarks

Applications of the Methodologies

Introduction

Results of the model: A general view

Model accuracy report

Scenarios of housing stress under various measures

Distribution of housing stress estimation

Lorenz curve for housing stress estimates

Proportional cumulative frequency graph and index of dissimilarity

Scenarios of households and housing stress by tenures

Estimation of households in housing stress by spatial scales

Results for different states

Results for various statistical divisions

Results for various statistical subdivisions

Small area estimates: Number of households in housing stress

Estimated numbers of overall households in housing stress

Estimated numbers of buyerhouseholds in housing stress

Estimated numbers of public renter households in housing stress

Estimated numbers of private renter households in housing stress

Estimated numbers of total renter households in housing stress

Small area estimates: Percentage of households in housing stress

Percentage estimates of housing stress for overall households

Percentage estimates of housing stress for buyer households

Percentage estimates of housing stress for public renter households

Percentage estimates of housing stress for private renter households

Percentage estimates of housing stress for total renter households

Concluding remarks

Analysis of Small Area Estimates in Capital Cities

Introduction

Scenarios of the results for major capital cities

Trends in housing stress for some major cities

Mapping the estimates at SLA levels within major cities

Sydney

Housing stress estimates for overall households

Small area estimation by household's tenure types

Melbourne

Housing stress estimates for overall households

Small area estimation by household's tenure types

Brisbane

Housing stress estimates for overall households

Small area estimation by household's tenure types

Adelaide

Housing stress estimates for overall households

Small area estimation by household's tenure types

Canberra

Housing stress estimates for overall households

Small area estimation by household's tenure types

Hobart

Housing stress estimates for overall households

Small area estimation by household's tenure types

Darwin

Housing stress estimates for overall households

Small area estimation by household's tenure types

Concluding remarks

Validation and Measure of Statistical Reliability

Introduction

Some validation methods in the literature

New approaches to validating housing stress estimation

Statistical significance test of the MMT estimates

Results of the statistical significance test

Absolute standardised residual estimate (ASRE) analysis

Results from the ASRE analysis

Measure of statistical reliability of the MMT estimates

Confidence interval estimation

Results from the estimates of confidence intervals

Concluding remarks

Conclusions and Computing Codes

Introduction

Summary of major findings

Limitations

Areas of further studies

Computing codes and programming

The general model file codes

SAS programming for reweithing algorithms

The second stage program file codes

Concluding remarks

Appendices.

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.