The book gathers information on the theories, applications, advantages, and limitations of all the small area estimation methodologies. It covers direct small area estimation methods, indirect statistical approaches, including empirical best linear unbiased prediction, empirical Bayes and hierarchical Bayes estimation methods.

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.