Introduction to Spatial Panel Data Models Using Stata

Many phenomena in the economics, medical and social fields, such as unemployment, crime rates or infectious diseases, tend to be spatially correlated. Spatial econometrics, in contrast to standard econometric modelling, exploits geo-referenced cross-sectional and/or panel data for dealing with spatial dependence and spatial heterogeneity. More specifically, spatial panel data sets contain repeated observations over time for a set of geo-referenced statistical units.

Our “Introduction to Spatial Panel Data analysis using Stata” course offers participants the opportunity to acquire the necessary theoretical and empirical toolset for modelling data which are correlated in time and space using both official and community written Stata spatial estimation commands. The opening session reviews Stata’s inbuilt sp command suite and illustrates how one prepares data for a spatial longitudinal analysis, before moving on to discuss different estimation techniques for both spatial fixed- and random-effects “static” models and for dynamic models with additive and/or interactive fixed-effects.

Throughout the course a series of empirical applications are used in order to highlight and discuss important issues such as model selection, average direct and indirect marginal effects, multiple spatial interactions and/or endogenous covariates, global stationarity, short-versus long-run marginal effects, and strong versus weak crosssectional dependence. Moreover, in common with TStat’s training 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 customizing the Stata do-file routines specifically developed for the course.

This course is of particular interest to 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 Spatial panel data estimation techniques in Stata.

Knowledge of the arguments covered in our “Introduction to Spatial Analysis using Stata”, “Linear Panel Data Models in Stata” and “Dynamic Panel Data Analysis” training courses is strongly suggested. Experience with Stata’s do-file programming is required.

SESSIONE I

1. Introduction
   • Spatial data analysis using Stata: overview of the sp suite
   • Space, spatial objects and spatial data
2. Preparing data for the spatial longitudinal analysis
   • Spatial and panel data declarations
   • Data with shapefile: Creating and merging a Stata-format shapefiles
   • Data without shapefile

SESSIONE II

1. Panel data models: first generation
   • The W (eights) matrix: types and normalization
   • Fixed- vs random- effects (static) models
   • Quasi Maximum Likelihood estimation
   • Hypothesis testing and model selection

SESSIONE III

1. First generation: further topics
   • Partial effects: direct, indirect and total effects
   • Fixed-effects Instrumental Variables estimation
     • (Selection) Internal instruments
     • Multiple spatial interactions and/or endogenous covariates

SESSIONE IV

1. Panel data models: second generation
   • Dynamic models
   • Estimation and testing
     • Global stationarity
     • Short- vs long-run marginal effects
     • Cross-sectional dependence (CD) and exponent of CD tests for Residuals

SESSIONE V

1. Panel data models: third generation
   • Dynamic models with weak and strong CD (Halleck Vega and Elhorst, 2016)
     • Quasi Maximum Likelihood estimation (Shi and Lee, 2017; Bai and Li, 2021)
   • Heterogeneous coefficients (Aquaro, Bailey and Pesaran, 2020)

We are currently adding the finishing touches to our 2024 training calendar. We therefore ask you to check our website regularly or contact us at training@tstat.eu should the dates for the course you are interested in not be published yet. You will then be contacted via email as soon as the dates are available.