COURSE OVERVIEW

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.

 

TARGET AUDIENCE

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 panel data estimation techniques in Stata.

 

COURSE REQUISITES

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.


PROGRAM

SESSIONE I

  • Introduction
    • Spatial data analysis using Stata: overview of the sp suite
    • Space, spatial objects and spatial data
  • 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

  • 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

  • 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

  • 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

  • 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)