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Short Course

There will be a two-day short course (June 16-17) right after the conference, targeting on graduate students, young researchers and applied researchers.  The course will be conducted by the four leading experts in the fields, in Seminar Room 5.1, School of Economics, Singapore Management University:
Giuseppe Arbia (16 June 9:00-12:30), Catholic University of the Sacred Heart, Rome, Italy          
Topics:  Typologies of spatial data in econometrics: Areal data, granular micro-data, flows data of spatial interaction. The weight matrix. The spatial lag definition. Measures of spatial correlation (Moran’s I, Geary’s C). Local indicators of Spatial Association. Moran scatterplot. Random fields. Some Markovian Random Fields: autobinomial, autoPoisson, Conditional autoregressive model (CAR).  Some non-Markovian fields: the Simultaneous Autoregressive model (SAR). Relationship between Car and SAR models.
Anil Bera (16 June 13:30-17:00), University of Illinois, Urbana-Champaign, USA 
Description:  We will start with a historical account of spatial analysis and quickly move to spatial econometric model building using real data. Our emphasis will be on testing spatial models.
Ingmar Prucha (17 June 9:00-12:30), University of Maryland, USA   
Description:  Spatial models have been important tools in economics, regional science and geography in analyzing a wide range of empirical issues. The lectures will focus, in particular, on Cliff-Ord type spatial models. Those models have the advantage that they only require a measure of distance for modeling interaction between cross sectional units, but do not require for the data to be indexed by location. Since distance is not limited to geographic distance, but could relate to distance in technological space, product space, social distance, etc., those models can be of interest for analyzing a wide range of network generated data. This includes the analysis of peer effects in social networks. The lectures will discuss and give an introduction to generalized methods of moments (GMM) and maximum likelihood (ML) estimation of spatial models from cross-sectional data, and will discuss tests for the presence of spatial/network interdependencies. 
Badi Baltagi (17 June 13:30-17:00), Syracuse University, USA
Topics:  Specification of spatial panel models. Estimation of Spatial Panel Models: Maximum Likelihood Estimation, Instrumental Variables and GMM . Testing for spatial dependence in spatial panels.
Fees for the Short Course (in US $):
  • Faculty and Professionals:           $300
  • Doctoral and Master Students:  $200
  • SMU Students:                                 $0 (no charge, registration required)
  • Other Singaporean Students:     $100
  • Coffee breaks and lunches will be covered.
To register click here 
(If you do not have a Paypal account, please checkout as guest at the payment page.)


Last updated on 19 Apr 2017 .