SPATIAL ECONOMETRICS ADVANCED INSTITUTE

Università Cattolica del Sacro Cuore of Rome, May 16th - June 10th 2016 (9th edition)

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Program:
Week I: 16th-20th May
Spatial Statistics (15 hours)
Instructor: Prof. G. Arbia, University "Cattolica del Sacro Cuore " of Rome.
Point processes theory (complete spatial randomness, distance methods, k-functions), multivariate point processes, marked point processes, space-time point patterns. Random fields theory, conditional and simultaneous Gaussian fields, Markov random fields, non Markov random fields. Stationary processes on a continuous space: variogram and co-variogram, the spectral representation, spatial prediction and krieging. Sptail interaction models.
Week II (23rd-27th May)
Spatial econometrics I (15 hours)
Instructor: Prof. Anil Bera, University of Illinois at Urbana-Champain.
Introduction to spatial econometric modelling.

Week III (30th may-3rd June)
Spatial econometrics II (15 hours)
Instructor: Prof. Ingmar Prucha, University of Maryland, College Park, Maryland.
Elements of large sample theory, single equation Cliff-Ord type models and variations, illustrations, specification, weighting matrix and parameter space issues, estimation including MLE, GMM, GLS, GS2SLS, large sample results and corresponding inferences,. Efficient instruments and best GS2SLS, prediction, estimation in case of heteroskedastic innovations by MLE, GMM, GS2SLS, large and small sample results. Simultaneous equation Cliff-Ord type models, estimation theory for limited and full information estimators. Spatial HAC variance covariance matrix estimation. Testing for spatial dependence, classical Moran I test and extensions. Recent developments towards estimation theory for nonlinear models.

Week IV (6th-10th June)
Panel data (15 hours of teaching)
Instructor: Prof. Badi H. Baltagi, Syracuse University, Syracuse, New York.
Panel data models: fixed effects and random effects. Temporal Heterogeneity. Spatial Seemingly Unrelated Regressions. Spatio-Temporal Models. Error Components with Space-Time Dependence. Specification of spatial panel models. Estimation of Spatial Panel Models: Maximum Likelihood Estimation, Instrumental Variables and GMM. Testing for spatial dependence in spatial panels.

References:
Anselin, L. (1988), Spatial Econometrics: Methods and Models. Boston: Kluwer Academic Publishers;
Anselin, L, Le Gallo, J., and Jayet, J. (2007) Spatial Panel Econometrics, In L. Matyas and P. Sevestre (Eds.), The Econometrics of Panel Data, Fundamentals and Recent Developments in Theory and Practice (3rd Edition). Dordrecht, Kluwer
Arbia, G. (2006), Spatial Econometrics: Statistical Foundations and Applications to Regional Growth Convergence, New York: Springer;
Arbia, G. (2014) A primer for spatial econometrics, Palgrave- MacMillan
Baltagi, B. H. (2008). Econometric Analysis of Panel Data (Fourth Edition). John Wiley & Sons, Chichester, United Kingdom.
Baltagi, B. H., Song, Seuck H., and Koh, W. (2003b). Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117:123–150.


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