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 cross-sectional and panel data collected with reference to location measured as points in space for dealing with spatial dependence and spatial heterogeneity. Our “Introduction to Spatial Analysis for Longitudinal Data” workshop offers researchers a unique opportunity to acquire the necessary theoretical and empirical toolset for the analysis of spatial longitudinal data, using the more recently developed spatial econometrics methodologies. The workshop begins by providing an overview of the more standard concepts in spatial econometrics and illustrating how one should prepare the data set for spatial analysis, before moving on to review the latest methodologies and commands (both official and user written commands) available in Stata. The workshop concludes by focusing on a number of more recent developments in spatial econometrics allowing simultaneously for serial dynamics, spatial spillovers and common factors. During the course of the workshop attention will also be given to the interpretation and presentation of results obtained.
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. At the end of the workshop, it is expected that participants are able identifying and evaluate which specific econometric method is more appropriate for the analysis in hand.
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 data analysis.
Knowledge of the arguments covered in our Introduction to Panel Data Analysis workshop, along with experience of Stata’s basic commands is required.
A taxonomy of spatial econometric models
Overview of the new Stata 15 sp suite of commands
Preparing data for the spatial longitudinal analysis:
Data with shapefiles: Creating and merging a Stata-format shapefiles (spshape2dta)
Spatial data declaration: spset
Data with coordinates of the geographical units spset, coord()
Balanced and unbalanced panels: spbalance
The W (eighting) matrix: creation, standardization and description using spmatrix
Quick spatial data visualization grmap
A taxonomy of spatial autoregressive models for panel data
Partial effects: direct, indirect and total effects
Estimation and interpretation of partial effects in static spatial autoregressive models for panel data using Quasi-ML (spxtregress):
Spatial Autoregressive model
Spatial Durbin model
Spatial error model
Spatial Autoregressive model with autoregressive error
Hypothesis testing and model selection
Estimation of static generalized spatial error models using Quasi-ML (xsmle)
Estimation of spatial autoregressive models for unbalanced panel data using Quasi-ML (mi estimate: xsmle)
Estimation of spatial autoregressive models for panel data using GMM (spxtivregress)
Estimation of dynamic spatial autoregressive models for panel data using Quasi-ML (xsmle):
Spatial autoregressive and Durbin models with time-lagged dependent variable
Spatial autoregressive and Durbin models with space-time lagged dependent variable
Spatial autoregressive and Durbin models with both time-lagged and space- time lagged dependent variable
Model selection and partial effects (long and short-run direct, indirect and total effects).
Recent developments in spatial panel data modeling
Spatial spillovers and common factors:
tests for strong cross- sectional dependence
one and two-stage approaches to spatial dynamic models with common factors (xsmle)
Spatial dynamic panel data models with interactive xed effects (xsmle).
The course will be held in Berlin from the 7th to the 9th October 2019.
Students*: € 735.00
Academic: € 1225.00
Non-Profit/Public Research Centres: € 1513.00
Commercial: € 1800.00
*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year.
Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number.
Please note that a non-refundable deposit of €100.00 for students and €200.00 for Academic, Non-Profit/Public Research Centres and Commercial participants, is required to secure a place and is payable upon registration. The number of participants is limited to 15. Places will be allocated on a first come, first serve basis.
Course fees cover: teaching materials (handouts, Stata do files and datasets to used during the course), a temporary licence of Stata valid for 30 days from the beginning of the workshop, light lunch and coffee breaks.
To maximize the usefulness of this workshop, we strongly recommend that participants bring their own laptops with them, to enable them to actively participate in the empirical sessions.
Individuals interested in attending this workshop must return their completed registration forms by email (firstname.lastname@example.org) to TStat by the 17th September 2019.
Our “Introduction to Spatial Analysis for Longitudinal Data” workshop offers researchers a unique opportunity to acquire the necessary theoretical and empirical toolset for the analysis of spatial longitudinal data, using the more recently developed spatial econometrics methodologies.