Science">
Econometría Espacial Programa Academico
Econometría Espacial Programa Academico
Econometría Espacial Programa Academico
Resumen
El curso explora los fundamentos de Econometría Espacial incluyendo herramientas de
georreferenciación que permiten realizar investigaciones de frontera en temas que incorporan
interacciones espaciales, efectos contagio, efectos pares, capital social, efectos vecindario o de redes (la
terminología usada dependerá del área de estudio y puede tener diferentes connotaciones). El temario
del curso es amplio presentando tópicos como: análisis exploratorio de datos espaciales, estimación e
interpretación de modelos espaciales de corte transversal, incluyendo modelos complejos como paneles
espaciales dinámicos. Al finalizar el curso se espera que el alumno sea capaz de construir y manipular
datos georreferenciados así como desarrollar alguna línea de investigación empírica usando los actuales
métodos de econometría espacial.
Programa a desarrollar
1. Introducción a la Econometría Espacial:
1
• Sesión Práctica 1: Introducción al Análisis Exploratorio Espacial usando GeoDa y
QuantumGIS.
• Sesión Práctica 2: Regresión por MCO y Tests de dependencia espacial usando GeoDa.
Evaluación
El sistema de evaluación para aprobar el curso consiste en la entrega obligatoria de un conjunto de
ejercicios prácticos y la realización de un Working Paper de tipo empírico aplicando algunos de los
tópicos desarrollados a lo largo del curso.
La nota final del curso tendrá una ponderación de:
• 50% nota de prácticos.
• 50% nota del Working Paper.
2
Periodo de Dictado de Clases
El curso se dictará en dos semanas diferentes:
Referencias bibliográficas
Las referencias Básicas permitirán un adecuado entendimiento de todo el material del curso, tanto de
la parte metodológica como aplicada. Las referencias Teóricas serán lecturas recomendadas para
profundizar contenido y se mencionarán a medida que se avance en el temario. Las referencias
Aplicadas permiten explorar las diferentes aplicaciones que pueden desarrollarse usando econometría
espacial y brindan un amplio temario sobre el posible tópico a desarrollar en el trabajo final.
• Básicas
1. Anselin, L. y A. Bera (1998). “Spatial dependence in linear regression models with
an Introduction to Spatial Econometrics,” en Ullah y Giles (eds), Handbook of Applied
Economic Statistics, Marcel Dekker, pp. 237-289.
2. Anselin, L. (2005). “Exploring spatial data with GeoDa: A workbook,” Spatial Analysis
Laboratory, CISS, University of Illinois, Urbana-Champaign.
3. Anselin, L., Le Gallo, J. y H. Jayet (2008). “Spatial panel econometrics,” en Matyas y
Sevestre (eds.), The Econometrics of Panel Data: Fundamentals and Recent Developments
in Theory and Practice, Berlin: Springer.
4. Brueckner, J. (2003). “Strategic interaction among governments: An overview of empirical
studies,” International Regional Science Review, 26(2), pp. 175-188.
5. Cressie, N. (1993). “Statistics for spatial data,” Chapter 1, Statistics for Spatial Data. New
York, Wiley.
6. Herrera, M. (2015). Econometría espacial usando Stata. Breve guía aplicada para datos de
corte transversal. Documentos de Trabajo del IELDE, 13.
7. Herrera, M. (2017). “Fundamentos de Econometría Espacial Aplicada”,
Capítulo en elaboración. Versión preliminar disponible en https://mpra.ub.uni-
muenchen.de/80871/1/MPRA_paper_80871.pdf.
8. Herrera, M. (2017). Econometría Espacial usando STATA: Guía Teórico - Aplicada. Versión
Actualizada, no publicada.
9. LeSage, J. (1999). The theory and practice of spatial econometrics. Matlab. University of
Toledo. Toledo, Ohio.
10. Elhorst, J. P. (2013). “Spatial Panel Models,” Chapter 82, en Fischer y Nijkamp (eds.)
Handbook of Regional Science. Heidelberg: Springer.
3
• Teóricas
1. Corte Transversal
(a) Anselin, L. (1988). Chapter 2 - Chapter 3 - Chapter 6, Spatial Econometrics: Methods
and Models. Boston: Kluwer Academic Publishers.
(b) Anselin, L. (2002). “Under the hood. Issues in the specification and interpretation of
spatial regression models,” Agricultural Economics, 27, pp. 247-267.
(c) Anselin, L. y D. Arribas-Bel (2013). “Spatial fixed effects and spatial dependence in a
single cross-section,” Papers in Regional Science, 92(1), pp. 3-17.
(d) Anselin, L., Bera, A., Florax, R. y M. Yoon (1996). “Simple diagnostic tests for spatial
dependence,” Regional Science and Urban Economics, 26, pp. 77-104.
(e) Barrios, T., Diamond, R., Imbens, G. y M. Kolesár (2012). “Clustering, spatial
correlations, and randomization inference,” Journal of the American Statistical
Association, 107(498), pp. 578-591.
(f) Farber, S., Paez, A. y E. Volz (2008). “Topology and Dependency Tests in Spatial and
Network Autoregressive Models,” Geographical Analysis, 41, pp. 158-180.
(g) Haining, R. (2004). “The nature of spatial data,” Chapter 2, Spatial Data Analysis.
theory and Practice, Cambridge.
(h) Kelejian, H e I. Prucha (1998). “A generalized spatial two-stage least squares procedure
for estimating a spatial autoregressive model with autoregressive disturbances,” Journal
of Real Estate Finance and Economics, 17(1), pp. 99-121.
(i) Kelejian, H. e I. Prucha (1999). “A generalized moments estimator for the
autoregressive parameter in a spatial model,” International Economic Review, 40(2),
pp. 509-533.
(j) Kelejian, H. e I. Prucha (2007). “HAC estimation in a spatial framework,” Journal of
Econometrics, 140, pp. 131-154.
(k) Leenders, R. T. A. (2002). “Modeling social influence through network autocorrelation:
constructing the weight matrix,” Social Networks, 24(1), pp. 21-47.
(l) LeSage, J. y R. Pace (2009). Chapter 1 - Chapter 2 - Chapter 3 - Chapter 4, Introduction
to Spatial Econometrics. Chapman & Hall/CRC.
(m) Miller, R. y P. Blair (2009). “Foundations of input-output analysis,” Chapter 2, Input-
Output Analysis: Foundations and Extensions. Cambridge, Cambridge University
Press.
(n) Mur, J. y A. Angulo (2009). “Model selection strategies in a spatial setting: Some
aditional results,” Regional Science and Urban Economics, 39, pp. 200-213.
(o) Oden, N. (1984). “Assessing the significance of a spatial correlogram,” Geographical
Analysis, 16(1), pp. 1-16.
(p) Smith, T. E. (2009). “Estimation bias in spatial models with strongly connected weight
matrices,” Geographical Analysis, 41(3), pp. 307-332.
2. Datos de Panel
(a) Debarsy, N., Ertur, C., & LeSage, J. P. (2012). “Interpreting dynamic space–time panel
data models,” Statistical Methodology, 9(1), pp. 158-171.
(b) Elhorst, J. P. (2001). “Dynamic models in space and time,” Geographical Analysis,
33(2), pp. 119-140.
(c) Lee, L.-f. y J. Yu (2010). “Estimation of spatial autoregressive panel data models with
fixed effects,” Journal of Econometrics, 154(2), 165–185.
(d) Vega, S. y J.P. Elhorst (2015). “The SLX model,” Journal of Regional Science, 55(3),
pp. 339-363.
4
• Aplicadas
1. Crecimiento
(a) Abreu, M., De Groot, H. L., & Florax, R. J. (2005). “Space and growth: a survey of
empirical evidence and methods,” Région et Développement, 21.
(b) Ertur, C. y W. Koch (2007). “Growth, technological interdependence and spatial
externalities: Theory and evidence,” Journal of Applied Econometrics, 22, pp. 1033-
1062.
(c) Rey, S. y B. Montouri (1999). “US regional income convergence: A spatial econometric
perspective”, Regional Studies, 33(2), pp. 143-156.
2. Cambio climático
(a) Nordhaus, W. (2006). “Geography and macroeconomics: New data and new findings.”
Proceedings of the National Academy of Sciences of the United States of America,
103(10), 3510-3517.
(b) Polsky, C. (2004), Putting Space and Time in Ricardian Climate Change Impact
Studies: Agriculture in the U.S. Great Plains, 1969–1992. Annals of the Association of
American Geographers, 94, pp. 549-564.
3. Causalidad
(a) Dubé, J., Legros, D., Thriault, M. y F. Rosiers (2014). “A spatial difference-
indifferences estimator to evaluate the effect of change in public mass transit systems
on house prices,” Transportation Research Part B: Methodological, 64, pp. 24-40.
(b) Herrera, M., Mur, J. y M. Ruiz (2016). “Detecting causal relationships between spatial
processes,” Papers in Regional Science, 95 (3), pp. 577-594.
4. Demografia
(a) Weeks, J., Getis, A., Hill, A., Gadalla, M. y T. Rashed (2004). “The fertility transition
in Egypt: Intraurban patterns in Cairo,” Annals of the Association of American
Geographers, 94, pp. 74-93.
5. Distribucion del ingreso
(a) Le Gallo, J. y C. Ertur (2003). “Exploratory spatial data analysis of the distribution
of regional per capita GDP in Europe, 1980− 1995,” Papers in regional science, 82(2),
pp. 175-201.
6. Economía del Crimen
(a) Baller, R., Anselin, L., Messner, S., Deane, G. y D. Hawkins (2001). “Structural
covariates of U.S. county homicide rates: Incorporating spatial effects,” Criminology,
39(3), 561-590.
(b) Ceccato, V. y R. Haining (2005). “Assessing the geography of vandalism: Evidence
from a Swedish city,” Urban Studies, 42(9), pp. 1637-1656.
(c) He, L., Páez, A. y D. Liu (2016). “Persistence of crime hot spots: An ordered probit
analysis,” Geographical Analysis, doi:10.1111/gean.12107.
(d) Menezes, T., Silveira-Neto, R., Monteiro, C. y J. Ratton (2013). “Spatial correlation
between homicide rates and inequality: Evidence from urban neighborhoods,”
Economics Letters, 120, pp. 97-99.
7. Finanzas
(a) Asgharian, H., Hess, W. y L. Liu (2013). “A spatial analysis of international stock
market linkages,” Journal of Banking & Finance, 37(12), pp. 4738-4754.
5
(b) Fernandez, V. (2011). “Spatial linkages in international financial markets,” Quantitative
Finance, 11(2), pp. 237-245.
(c) Hassett, K. y A. Mathur (2015). “A spatial model of corporate tax incidence,” Applied
Economics, 47(13), pp. 1350-1365.
8. Mercado Inmobiliario
(a) Fingleton, B. (2006). “A cross-sectional analysis of residential property prices: the
effects of income, commuting, schooling, the housing stock and spatial interaction in
the English regions,” Papers in Regional Science, 85(3), pp. 339-361.
9. Pobreza
(a) Camara, G., Sposati, A., Koga, D., Monteiro, A., Roman, F., Camargo, E. y S. Druck
(2004). “Mapping social exclusion and inclusion in developing countries. Spatial
patterns of Sao Paulo in the 1990s,” en Goodchild, M. y D. Janelle (eds.), Spatially
Intergrated Social Science, Oxford University Press, pp. 223-238.
(b) Jung, S., Cho, S. y R. Roberts (2014). “The impact of government funding of poverty
reduction programmes”, Papers in Regional Science, 94(3), pp. 653-675.
10. R&D
(a) Hammadou, H., Paty, S. y M. Savona (2014). “Strategic interactions in public R&D
across European countries: A spatial econometric analysis,” Research Policy, 43(7), pp.
1217-1226.
(b) Montmartin, B. y M. Herrera (2015). “Internal and external effects of R&D subsidies
and fiscal incentives: Empirical evidence using spatial dynamic panel models,” Research
Policy, 44 (5), pp. 1065-1079.
(c) Paci, R., Marrocu, E. y S. Usai (2014). “The complementary effects of proximity
dimensions on knowledge spillovers”, Spatial Economic Analysis, 9(1), pp. 9-30.
11. Redes sociales - Telecomunicaciones
(a) Lansley, G., & Longley, P. A. (2016). “The geography of Twitter topics in London,”
Computers, Environment and Urban Systems, 58, pp. 85-96.
(b) Sagl, G., Delmelle, E. y E. Delmelle (2014). “Mapping collective human activity
in an urban environment based on mobile phone data”, Cartogaphy and Geographic
Information Science, 41(3), pp. 272-285.
(c) Shelton, T., Poorthuis, A., y M. Zook (2015). “Social media and the city: Rethinking
urban socio-spatial inequality using user-generated geographic information,” Landscape
and Urban Planning, 142, pp. 198-211.
12. Salud
(a) Gravelle, H., Santos, R. y L. Siciliani (2014). “Does a hospital’s quality depend on
the quality of other hospitals? A spatial econometrics approach,” Regional Science and
Urban Economics, 49, pp. 203-216.
(b) Tu, W., Tedders, S. y J. Tian (2012). “An exploratory spatial data analysis of low birth
weight prevalence in Georgia”, Applied Geography, 32, pp. 195-207.
13. Otros
(a) Garret, T. y T. Marsh (2002). “The revenue impacts of cross-border lottery shopping
in the presence of spatial autocorrelation,” Regional Science and Urban Economics, 32,
pp. 501-519.
(b) Kalnins, A. (2003). “Hamburger prices and spatial econometrics,” Journal of Economics
& Management Strategy, 12(4), pp. 591-616.
(c) Keller, W. y C. Shiue (2007). “The origins of spatial interaction,” Journal of
Econometrics, 140(1), pp. 304-332.