Giuseppe Arbia (PhD Cantab) is Full Professor of Economic Statistics at the Università Cattolica del Sacro Cuore of Rome, Italy, and Lecturer of Statistics at the Università della Svizzera Italiana in Lugano, Switzerland. He was formerly Visiting Professor at New York University, USA, and at many other international universities. He has published five books and more than 100 articles on the topic of spatial statistics and econometrics and is currently the Chairman of the Spatial Econometrics Association and the Director of the Spatial Econometrics Advanced Institute.

“The book is a very useful practical guide for applied researchers employing spatial econometrics tools. It could be adopted as the textbook for a first course in spatial economics/econometrics, and it comes as an easy-to-read book for students and researchers with a little knowledge in this field. … it presents both basic concepts and more advanced topics in a synthetic but clear and rigorous manner, and this approach makes it an useful reference for applied (spatial) econometricians.” (Roberto Ganau, Italian Journal of Regional Science, Vol. 16 (1), January, 2017)

• 1. The Classical Linear Regression Model 2. Some Important Spatial Definitions 3. Spatial Linear Regression Models 4. Further Topics in Spatial Econometrics 5. Alternative Model Specifications for Big Datasets 6. Conclusions: What's Next?

Giuseppe Arbia (PhD Cantab) is Full Professor of Economic Statistics at the Università Cattolica del Sacro Cuore of Rome, Italy, and Lecturer of Statistics at the Università della Svizzera Italiana in Lugano, Switzerland. He was formerly Visiting Professor at New York University, USA, and at many other international universities. He has published five books and more than 100 articles on the topic of spatial statistics and econometrics and is currently the Chairman of the Spatial Econometrics Association and the Director of the Spatial Econometrics Advanced Institute.

“The book is a very useful practical guide for applied researchers employing spatial econometrics tools. It could be adopted as the textbook for a first course in spatial economics/econometrics, and it comes as an easy-to-read book for students and researchers with a little knowledge in this field. … it presents both basic concepts and more advanced topics in a synthetic but clear and rigorous manner, and this approach makes it an useful reference for applied (spatial) econometricians.” (Roberto Ganau, Italian Journal of Regional Science, Vol. 16 (1), January, 2017)

• 1. The Classical Linear Regression Model 2. Some Important Spatial Definitions 3. Spatial Linear Regression Models 4. Further Topics in Spatial Econometrics 5. Alternative Model Specifications for Big Datasets 6. Conclusions: What's Next?

Giuseppe Arbia (PhD Cantab) is Full Professor of Economic Statistics at the Università Cattolica del Sacro Cuore of Rome, Italy, and Lecturer of Statistics at the Università della Svizzera Italiana in Lugano, Switzerland. He was formerly Visiting Professor at New York University, USA, and at many other international universities. He has published five books and more than 100 articles on the topic of spatial statistics and econometrics and is currently the Chairman of the Spatial Econometrics Association and the Director of the Spatial Econometrics Advanced Institute.

“The book is a very useful practical guide for applied researchers employing spatial econometrics tools. It could be adopted as the textbook for a first course in spatial economics/econometrics, and it comes as an easy-to-read book for students and researchers with a little knowledge in this field. … it presents both basic concepts and more advanced topics in a synthetic but clear and rigorous manner, and this approach makes it an useful reference for applied (spatial) econometricians.” (Roberto Ganau, Italian Journal of Regional Science, Vol. 16 (1), January, 2017)

• 1. The Classical Linear Regression Model 2. Some Important Spatial Definitions 3. Spatial Linear Regression Models 4. Further Topics in Spatial Econometrics 5. Alternative Model Specifications for Big Datasets 6. Conclusions: What's Next?