.... "I will be teaching this course during October-December, 2020 at Universidad de Talca"
Spatial Econometrics
Course Description
This course is a lecture-based introduction to the methodology and application of spatial econometric models for Master and Ph.D. students. We will learn topics such as model specification and model choice, as well as specification testing in a spatial econometric framework with cross-sectional data.
The course will consists of lecture as well as computer exercises where we will learn how to use the free software R and Geoda in order to estimate spatial econometric models.
This class will be a bit intensive in terms of programming. One the goal of the course is to give the programming tools in R so that students should be able to code the algorithm for estimating the basic spatial models.
Software
R can be downloaded using this link . Another important program that might be useful is Rstudio. This is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. Rstudio can be downloaded at here.
We will use also the Geoda software, which is a free, open source, cross-platform software program that serves as an introduction to spatial data analysis. This can be downloaded at here.
Course Reading:
This course is a lecture-based introduction to the methodology and application of spatial econometric models for Master and Ph.D. students. We will learn topics such as model specification and model choice, as well as specification testing in a spatial econometric framework with cross-sectional data.
The course will consists of lecture as well as computer exercises where we will learn how to use the free software R and Geoda in order to estimate spatial econometric models.
This class will be a bit intensive in terms of programming. One the goal of the course is to give the programming tools in R so that students should be able to code the algorithm for estimating the basic spatial models.
Software
R can be downloaded using this link . Another important program that might be useful is Rstudio. This is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. Rstudio can be downloaded at here.
We will use also the Geoda software, which is a free, open source, cross-platform software program that serves as an introduction to spatial data analysis. This can be downloaded at here.
Course Reading:
- Introduction to Spatial Econometrics
- Anselin, L. (1995). Local Indicators of Spatial Association- LISA. Geographical analysis, 27(2), 93-115.
- Celebioglu, F., & Dall'Erba, S. (2010). Spatial Disparities Across the Regions of Turkey: An Exploratory Spatial Data Analysis. The Annals of Regional Science,45(2), 379-400.
- Dall'Erba, S. (2005). Distribution of Regional Income and Regional Funds in Europe 1989–1999: An Exploratory Spatial Data Analysis. The Annals of Regional Science, 39(1), 121-148.
- Stewart, B. M., & Zhukov, Y. (2010, February). Choosing Your Neighbors: The Sensitivity of Geographical Diffusion in International Relations. In APSA 2010 Annual Meeting Paper.
- Spatial Models
- Anselin, L. (2003). Spatial Externalities, Spatial Multipliers, and Spatial Econometrics. International regional science review, 26(2), 153-166.
- Elhorst, J. P. (2010). Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis, 5(1), 9-28.
- Kirby, D. K., & LeSage, J. P. (2009). Changes in commuting to work times over the 1990 to 2000 period. Regional Science and Urban Economics, 39(4), 460-471.
- Maximum Likelihood Estimation
- Ord, K. (1975). Estimation Methods for Models of Spatial Interaction. Journal of the American Statistical Association, 70(349), 120-126.
- Anselin, L., & Bera, A. K. (1998). Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics. Statistics Textbooks and Monographs, 155, 237-290.
- Garrett, T. A., & Marsh, T. L. (2002). The Revenue Impacts of Cross-Border Lottery Shopping in the Presence of Spatial Autocorrelation. Regional Science and Urban Economics, 32(4), 501-519.
- Anselin, L. (1990). Some Robust Approaches to Testing and Estimation in Spatial Econometrics. Regional Science and Urban Economics, 20(2), 141-163.
- Boxall, P. C., Chan, W. H., & McMillan, M. L. (2005). The Impact of Oil and Natural Gas Facilities on Rural Residential Property Values: A Spatial Hedonic Analysis. Resource and energy economics, 27(3), 248-269.
- Bivand, R. (2011). After 'Raising the Bar' : Applied Maximum Likelihood Estimation of Families of Models in Spatial Econometrics. NHH Dept. of Economics Discussion Paper, (22).
- Ertur, C., & Koch, W. (2007). Growth, Technological Interdependence and Spatial Externalities: Theory and Evidence. Journal of applied econometrics, 22(6), 1033-1062.
- Lee, L. F. (2004). Asymptotic Distributions of Quasi‐Maximum Likelihood Estimators for Spatial Autoregressive Models. Econometrica, 72(6), 1899-1925.
- Boarnet, M. G., & Glazer, A. (2002). Federal Grants and Yardstick Competition.Journal of urban Economics, 52(1), 53-64.
- IV and GMM Estimators
- Messner, S. F., & Anselin, L. (2004). Spatial Analyses of Homicide with Areal Data. Spatially integrated social science, 12.
- Kelejian, H. H., & Prucha, I. R. (1998). A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. The Journal of Real Estate Finance and Economics, 17(1), 99-121.
- Kelejian, H. H., & Prucha, I. R. (1999). A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model. International economic review,40(2), 509-533.
- Piras, G. (2010). sphet: Spatial Models with Heteroskedastic Innovations in R.Journal of Statistical Software.
- Bivand, Roger, & Gianfranco Piras (2015). Comparing Implementations of Estimation Methods for Spatial Econometrics. Journal of Statistical Software 63.i18.
- Kelejian, H. H., & Prucha, I. R. (2010). Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances.Journal of Econometrics, 157(1), 53-67.
- Arraiz, I., Drukker, D. M., Kelejian, H. H., & Prucha, I. R. (2010). A Spatial Cliff‐Ord‐Type Model with Heteroskedastic Innovations: Small and Large Sample Results. Journal of Regional Science, 50(2), 592-614.