Many phenomena in the fields of economics, medical and social science, such as unemployment, crime rates or infectious diseases tend to be spatially correlated. Spatial econometrics has developed to include techniques and methods to model the spatial characteristics of such data, by taking into account both spillover effects and spatial heterogeneity.
Our “Introduction to Spatial Analysis using Stata” course offers researchers a unique opportunity to acquire the necessary toolset to conduct exploratory spatial data analysis. The course begins by providing an overview of Stata’s sp suite of commands for spatial analysis and then discusses both how to manage different kind of spatial data and how to prepare spatial data for empirical analysis. The course moves on to focus on spatial data visualization, how to define proximity using spatial weights matrices and how to detect spatial autocorrelation. In the closing sessions participants are introduced to spatial autoregressive models, more specifically on the concepts of estimation, testing and model selection. Special emphasis is given to the computation and interpretation of average direct and indirect marginal effects and to the treatment of special cases such as multiple spatial interactions and more endogenous covariates.
In common with TStat’s course philosophy, each individual session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced by case study examples, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. Particular attention is also given to both the interpretation and presentation of empirical results.
Upon completion of the course, it is expected that participants are able to identify and evaluate which specific spatial econometric methodology is more appropriate to both their dataset and the analysis in hand and subsequently apply the selected estimation techniques to their own data.
Ph.D. Students, researchers and professionals working in public and private institutions interested in acquiring the latest empirical techniques to be able to independently implement spatial data analysis.
Knowledge of basic econometrics tools such as ordinary least-squares, instrumental variables and maximum likelihood estimation of the linear regression model is strongly recommended. A basic knowledge of Stata’s do-file programming is required.
Spatial data analysis using Stata: an overview of the sp suite
Space, spatial objects and spatial date
Preparing data for the spatial analysis:
Spatial data declaration
Data with shapefile: Creating and merging a Stata-format shapefiles
Data without shapefile
Visualizing spatial data:
Geographic coordinate systems
2D spatial point patterns
Change coordinate system
Measure spatial proximity:
The W (eights) matrix
Detect spatial autocorrelation
Spatial autoregressive models I:
Quasi Maximum Likelihood estimation
Hypothesis testing and model selection
Spatial autoregressive models II:
Partial effects: direct, indirect and total effects
Generalized method of moments estimation
Multiple endogenous covariates
Multiple spatial lags
We are currently putting the finishing touches to our 2023 training calendar. We therefore ask that you re-visit our website periodically or contact us at email@example.com should the dates for the course which you are interested in following not yet be published. You will then be contacted via email as soon as the dates are available.
Our “Introduction to Spatial Analysis using Stata” course offers researchers a unique opportunity to acquire the necessary toolset to conduct exploratory spatial data analysis. The course begins by providing an overview of Stata’s sp suite of commands for spatial analysis and then discusses both how to manage different kind of spatial data and how to prepare spatial data for empirical analysis.