Spatial Econometrics

The methods of spatial econometrics allow to take account of interactions between investigated units in the modeling, i.e. remove the assumption of observational independence. The underlying concepts of this functional subdiscipline of econometrics will be presented: spatial weight matrices, testing of spatial effects, model specifications and consistent estimation methods, as well as spatial multipliers and some special models. The classes in computer laboratory will Focus on visualisation in maps and working with the models in consideration in the {spdep}/{spatialreg}/{splm} packages in R.

- Students should become familiar with the basic notions of spatial econometrics (in cross-section approach): spatial weight matrix, tests of spatial effects, SAR, SEM, SLM, SARAR and Durbin models, spatial multipliers (direct, indirect, total).

- Presentation of the main areas of development in the spatial econometrics, as well as basic definitions and procedures that will facilitate individual studies of specific topics by the students in the future (panel models, discrete choice models, models with spatial heteroskedasticity).

- Mastering the skills of visualising spatial data with R and using maps and datasets available in European geostatistical portals.

- Developing the capability of specifying and estimating spatial econometric models in R, including: importing maps and datasets in a correct format, merging tables, creating weight matrices, estimating a selected model and finding the optimum model specification.

- Developing the skill of correct parameter interpretation in the spatial models.

Literatura podstawowa:

Arbia, G. (2014), A Primer for Spatial Econometrics With Applications in R, Palgrave Macmillan, Houndmills/Nowy Jork.

Literatura uzupełniająca:

1. Elhorst, J.P. (2014), Spatial Econometrics. From Cross-Sectional Data to Spatial Panels, Springer.

2. Kelejian, H., Piras, G. (2017), Spatial Econometrics, Elsevier.

3. LeSage, J., Pace, R.K. (2009), Introduction to Spatial Econometrics, Chapman and Hall/CRC, Boca Raton.

4. Yamagata, Y.,Seya, H. (2020), Spatial Analysis Using Big Data, Elsevier.

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

referaty/eseje: 50.00%

The methods of spatial econometrics allow to take account of interactions between investigated units in the modeling, i.e. remove the assumption of observational independence. The underlying concepts of this functional subdiscipline of econometrics will be presented: spatial weight matrices, testing of spatial effects, model specifications and consistent estimation methods, as well as spatial multipliers and some special models. The classes in computer laboratory will Focus on visualisation in maps and working with the models in consideration in the {spdep}/{spatialreg}/{splm} packages in R.

- Students should become familiar with the basic notions of spatial econometrics (in cross-section approach): spatial weight matrix, tests of spatial effects, SAR, SEM, SLM, SARAR and Durbin models, spatial multipliers (direct, indirect, total).

- Presentation of the main areas of development in the spatial econometrics, as well as basic definitions and procedures that will facilitate individual studies of specific topics by the students in the future (panel models, discrete choice models, models with spatial heteroskedasticity).

- Mastering the skills of visualising spatial data with R and using maps and datasets available in European geostatistical portals.

- Developing the capability of specifying and estimating spatial econometric models in R, including: importing maps and datasets in a correct format, merging tables, creating weight matrices, estimating a selected model and finding the optimum model specification.

- Developing the skill of correct parameter interpretation in the spatial models.

Literatura podstawowa:

Arbia, G. (2014), A Primer for Spatial Econometrics With Applications in R, Palgrave Macmillan, Houndmills/Nowy Jork.

Literatura uzupełniająca:

1. Elhorst, J.P. (2014), Spatial Econometrics. From Cross-Sectional Data to Spatial Panels, Springer.

2. Kelejian, H., Piras, G. (2017), Spatial Econometrics, Elsevier.

3. LeSage, J., Pace, R.K. (2009), Introduction to Spatial Econometrics, Chapman and Hall/CRC, Boca Raton.

4. Yamagata, Y.,Seya, H. (2020), Spatial Analysis Using Big Data, Elsevier.

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

referaty/eseje: 50.00%

The methods of spatial econometrics allow to take account of interactions between investigated units in the modeling, i.e. remove the assumption of observational independence. The underlying concepts of this functional subdiscipline of econometrics will be presented: spatial weight matrices, testing of spatial effects, model specifications and consistent estimation methods, as well as spatial multipliers and some special models. The classes in computer laboratory will Focus on visualisation in maps and working with the models in consideration in the {spdep} package in R.

- Students should become familiar with the basic notions of spatial econometrics (in cross-section approach): spatial weight matrix, tests of spatial effects, SAR, SEM, SLM, SARAR and Durbin models, spatial multipliers (direct, indirect, total).

- Presentation of the main areas of development in the spatial econometrics, as well as basic definitions and procedures that will facilitate individual studies of specific topics by the students in the future (panel models, discrete choice models, models with spatial heteroskedasticity).

- Mastering the skills of visualising spatial data with R and using maps and datasets available in European geostatistical portals.

- Developing the capability of specifying and estimating spatial econometric models in R, including: importing maps and datasets in a correct format, merging tables, creating weight matrices, estimating a selected model and finding the optimum model specification.

- Developing the skill of correct parameter interpretation in the spatial models.

Literatura podstawowa:

Arbia, G. (2014), A Primer for Spatial Econometrics With Applications in R, Palgrave Macmillan, Houndmills/Nowy Jork.

Literatura uzupełniająca:

1. Elhorst, J.P. (2014), Spatial Econometrics. From Cross-Sectional Data to Spatial Panels, Springer.

2. Kelejian, H., Piras, G. (2017), Spatial Econometrics, Elsevier.

3. LeSage, J., Pace, R.K. (2009), Introduction to Spatial Econometrics, Chapman and Hall/CRC, Boca Raton.

4. Yamagata, Y.,Seya, H. (2020), Spatial Analysis Using Big Data, Elsevier.

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

referaty/eseje: 50.00%

The methods of spatial econometrics allow to take account of interactions between investigated units in the modeling, i.e. remove the assumption of observational independence. The underlying concepts of this functional subdiscipline of econometrics will be presented: spatial weight matrices, testing of spatial effects, model specifications and consistent estimation methods, as well as spatial multipliers and some special models. The classes in computer laboratory will Focus on visualisation in maps and working with the models in consideration in the {spdep} package in R.

- Students should become familiar with the basic notions of spatial econometrics (in cross-section approach): spatial weight matrix, tests of spatial effects, SAR, SEM, SLM, SARAR and Durbin models, spatial multipliers (direct, indirect, total).

- Presentation of the main areas of development in the spatial econometrics, as well as basic definitions and procedures that will facilitate individual studies of specific topics by the students in the future (panel models, discrete choice models, models with spatial heteroskedasticity).

- Mastering the skills of visualising spatial data with R and using maps and datasets available in European geostatistical portals.

- Developing the capability of specifying and estimating spatial econometric models in R, including: importing maps and datasets in a correct format, merging tables, creating weight matrices, estimating a selected model and finding the optimum model specification.

- Developing the skill of correct parameter interpretation in the spatial models.

Literatura podstawowa:

Arbia, G. (2014), A Primer for Spatial Econometrics With Applications in R, Palgrave Macmillan, Houndmills/Nowy Jork.

Literatura uzupełniająca:

1. Elhorst, J.P. (2014), Spatial Econometrics. From Cross-Sectional Data to Spatial Panels, Springer.

2. Kelejian, H., Piras, G. (2017), Spatial Econometrics, Elsevier.

3. LeSage, J., Pace, R.K. (2009), Introduction to Spatial Econometrics, Chapman and Hall/CRC, Boca Raton.

4. Yamagata, Y.,Seya, H. (2020), Spatial Analysis Using Big Data, Elsevier.

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

referaty/eseje: 50.00%