Online, Italy

Introduction to Spatial Panel Data Models Using Stata

online course
when 17 May 2021 - 21 May 2021
language English
duration 1 week
fee EUR 890

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 built-in 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.

During the five sessions of 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 cross-sectional dependence.

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.

Target group

Ph.D. Students, researchers and professionals working in public and private institutions interesting in acquire the latest empirical techniques to be able to independently implement spatial panel data estimation techniques in Stata.

Course aim

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 built-in 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.

Fee info

EUR 890: Full-Time Students*: € 890.00
Academic: € 1260.00
Commercial: € 1685.00

*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year.