Spatial Microeconometrics introduces the reader to the basic concepts of spatial statistics, spatial econometrics and the spatial behavior of economic agents at the microeconomic level. Incorporating useful examples and presenting real data and datasets on real firms, the book takes the reader through the key topics in a systematic way.

The book outlines the specificities of data that represent a set of interacting individuals with respect to traditional econometrics that treat their locational choices as exogenous and their economic behavior as independent. In particular, the authors address the consequences of neglecting such important sources of information on statistical inference and how to improve the model predictive performances. The book presents the theory, clarifies the concepts and instructs the readers on how to perform their own analyses, describing in detail the codes which are necessary when using the statistical language R.

The book is written by leading figures in the field and is completely up to date with the very latest research. It will be invaluable for graduate students and researchers in economic geography, regional science, spatial econometrics, spatial statistics and urban economics.

�ber die Autorin bzw. den Autor

Giuseppe Arbia is full professor of economic statistics at the Faculty of Economics, Catholic University of the Sacred Heart, Rome, and lecturer at the University of Italian Switzerland in Lugano. Since 2006 he has been president of the Spatial Econometrics Association and since 2008 he has chaired the Spatial Econometrics Advanced Institute. He is also a member of many international scientific societies.

Giuseppe Espa is full professor in economic statistics at the Department of Economics and Management of the University of Trento and the LUISS "Guido Carli" University of Rome.

Diego Giuliani is associate professor in economic statistics at the Department of Economics and Management of the University of Trento. He works primarily on the use and development of statistical methods to analyze firm-level micro-geographic data.

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