HOME PAGE

PROGRAM

OUR ACADEMICS

ADMISSION

LOCATION

PAST EDITIONS

PROGRAM

Week I (21st-25th June)
Econometrics (10 hours of teaching)
Instructor: Prof. Raffaella Giacomini, University College London.
Spatial Statistics (12 hours of teaching)
Instructor: Prof. G. Arbia, University “G. D’Annunzio” of Chieti.

Week II (28th June - 2nd July)
Spatial econometrics I (15 hours of teaching + 10 hours lab)
Instructor: Prof. Harry H. Kelejian, University of Maryland, College Park, Maryland.

Week III (5th to 9th July)
Spatial econometrics II (15 hours of teaching + 10 hours lab)
Instructor: Prof. Ingmar Prucha, University of Maryland, College Park, Maryland.

Week IV (12th to 16th July)
Spatial panel data (15 hours of teaching + 10 hours lab)
Instructor: Prof. Badi H. Baltagi, Syracuse University, Syracuse, New York.

Final exam, 17th July (not compulsory)

Provisional week schedule (download)

PRE-COURSES:
Upon demand pre-courses on basic econometrics and R will be held the week before the starting of the courses (16th-18th June, 2010).

CERTIFICATION:
Participants to the SEAI will receive a certificate of attendance to the courses and a final exam certificate (for those taking it).

BEST STUDENT AWARD:
A financial prize is awarded yearly to the best student in the SEAI. The ranking is based on the results of the (optional) final exam.

DETAILED CONTENT OF THE COURSES:

Econometrics (R. Giacomini, 10 hours)
- The classical linear regression model. OLS estimation. Finite-sample properties of OLS. Hypothesis testing under normality Readings. GLS estimation. Large-sample theory. Distribution of OLS estimator. Asymptotic variance estimation. Serial correlation testing. Extremum estimators. Consistency and asymptotic normality . Special cases: GMM and ML. Testing the overidentifying restrictions. Likelihood ratio tests. Special cases of GMM and ML: 2SLS, discrete choice models, truncated regression models, tobit models.
References: Hayashi (2000), Econometrics, Princeton University Press. Alternative books that cover most of the topics in the lectures are: Johnston and DiNardo (1997), Econometric Methods, 4th ed., McGraw-Hill. Goldberger (1991), A Course in Econometrics, Harvard University Press. Greene (2002), Econometric Analysis, Prentice Hall. Wooldridge (2002), Econometric Analysis of Cross Section and Panel Data, MIT press.

Spatial Statistics (G. Arbia, 12 hours)
- 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, dynamic fields, separable and non-separable space-time models. Stationary processes on a continuous space: variogram and co-variogram, the spectral representation, spatial prediction and krieging.
References: Banerjee, S., Carlin, B. P., and Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC, Boca Raton, FL. Cressie, N (1993) Statistics for spatial data, Wiley. Diggle, P.J. (2003). Statistical Analysis of Spatial Point Patterns (second edition). London: Edward Arnold. Diggle, P.J. and Ribeiro, P.J. Jnr (2007). Model-based Geostatistics. New York: Springer. Guyon X. (1995) Random fields on a network: modeling, statistics, and applications, Springer Verlag. Haining R P (2001) Spatial Data Analysis: Theory and Practice, Cambridge University Press.

Spatial Econometrics 1 (H. Kelejian, 15 hours)
- 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, emanating and self feedback effects implied by the models, various estimation problems including border issues, uniform weights, and parameterized weighting matrices, a spatial J-Test of specifications. References: Anselin, L. (1988), Spatial Econometrics: Methods and Models. Boston: Kluwer Academic Publishers; Arbia, G. (2006), Spatial Econometrics: Statistical Foundations and Applications to Regional Growth Convergence, New York: Springer; Cliff, A. and Ord, J. (1981), Spatial Processes, Models and Applications. London: Pion; Cressie, N.A.C. (1993), Statistics of Spatial Data. New York: Wiley; Green, W. (2003), Econometric Analysis, Englewood Cliffs: Prentice Hall.

Spatial Econometrics 2 (I. Prucha, 15 hours)
- Further discussion of single equation Cliff-Ord type models, 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, if time permits. References: Anselin, L. (1988), Spatial Econometrics: Methods and Models. Boston: Kluwer Academic Publishers; Arbia, G. (2006), Spatial Econometrics: Statistical Foundations and Applications to Regional Growth Convergence, New York: Springer; Cliff, A. and Ord, J. (1981), Spatial Processes, Models and Applications. London: Pion; Cressie, N.A.C. (1993), Statistics of Spatial Data. New York: Wiley; Green, W. (2003), Econometric Analysis, Englewood Cliffs: Prentice Hall; articles.

Spatial panel data (B. Baltagi, 15 hours)
- 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, 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. Baltagi, B. H. (2001). Econometric Analysis of Panel Data (Second 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.

Computer lab (G. Piras, 35 hours)
- Practical experiences in spatial econometrics using the R software. References: An introduction to R (both the software and the introductory manual may be downloaded for free via http://www.r-project.org/)

Pre-course in statistics and econometrics background (12 hours): The pre-course is intended to introduce the participants to the basic econometric methods and statistical techniques that will be discussed further during the courses. For more information on pre-requisites go to: admission.

Pre-course in R (12 hours): The pre-course is directed to the participants that are not familiar with the software and it aims at introducing the most basic routines of R and their applications for spatial statistics and econometrics.

Final exam: You can download the questions provided for the exam of the 2009 edition (download) and of the 2008 edition (download).

For more information contact Giuseppe Arbia at arbia@unich.it