Curso metodológico de regression especial

Del 12 al 16 de diciembre, el professor Jude Hays (University of Pittsburgh) impartirá un curso de cinco días y 15 horas de duración sobre regresión espacial.

El curso se impartirá en inglés, es gratuito y se dirige a los científicos sociales (profesores, investigadores, estudiantes) que estén interesados en analizar datos con dependencia espacial.

Todas las clases serán por la tarde, de 15:30 a 18:30.

Asistencia

Las personas interesadas en asistir al curso deberían contactar con Magdalena Nebreda (magdalen@march.es) antes del 22 de noviembre, enviando un CV y una breve explicación de su interés en el curso. Si se reciben muchas solicitudes será preciso realizar un proceso de selección. La confirmación definitiva de asistencia al curso se enviará el 25 de noviembre.

Course Description. Spatial interdependence is ubiquitous throughout the social sciences. This is particularly true when we expand our conception of space beyond physical geography to include cultural, political and economic notions of distance.  The likelihood and outcomes of demonstrations, riots, coups, and revolutions in one country almost certainly depend in substantively crucial ways on such occurrences in other countries (e.g., through demonstration effects or snowballing). Election outcomes and candidate qualities or strategies in some contests surely depend on those in others, and representatives’ votes in legislatures certainly depend on others’ votes or expected votes. In micro-behavioral research, long-standing and recently surging interest in contextual or network effects often refers to the effects on each individual’s behavior or opinion from sets of other individuals’ opinions or behaviors; e.g., a respondent’s opinion on some policy likely depends on the opinions of her state, district, community, or social group. In international relations, states’ entry decisions in wars, alliances, and organizations, e.g., heavily depend on how many and who else enters and how. In comparative and international political economy, globalization, i.e., international economic integration, implies strategic (and non-strategic) interdependence in national-level macroeconomic policymaking. This course introduces spatial and spatiotemporal econometric models for continuous and limited dependent variables that can address such interdependence, with an emphasis on social-science applications.

Course Objectives. The main objective of this course is to teach students how to incorporate the interdependence implied by most social scientific theories into their empirical analysis. Students will learn inter alia how to 1) diagnose spatial patterns in their data, 2) estimate the structural parameters of spatial and spatiotemporal regression models, 3) calculate and present spatial and spatiotemporal effects, and 4) use spatial modeling to discriminate between the multiple sources of spatial correlation—common exposure, interdependence, and selection—and, when it exists, to evaluate the nature of the interdependence (e.g., strategic free-riding behavior, learning, coercion) among units of observation.

Course Schedule with References

Anselin (2006) and Franzese and Hays (2008) provide overviews of all but a few of the topics we will cover. Ward and Gleditsch (2008) is an excellent introductory textbook for spatial regression models.

Anselin, L. 2006. Spatial Econometrics. In T.C. Mills and K. Patterson, eds., Palgrave Handbook of Econometrics: Volume 1, Econometrics Theory. Basingstoke: Palgrave Macmillan, pp. 901-941.

Franzese, R., Hays, J. 2008. Empirical Models of Spatial Interdependence. In J. Box-Steffensmeier, H. Brady, D. Collier, eds., Oxford Handbook of Political Methodology, Oxford UP, pp. 570-604. (Use the hyperlinked version; it corrects an error in the printed version.)

Ward, M.D. and K.S. Gleditsch. 2008. Spatial Regression Models. Thousand Oaks, CA: Sage.

1) Monday, December 12^th

Introductory Stuff, Theoretical and Empirical Models of “Spatial” Interdependence

References:

Ross, M. H. and E. Homer. 1976.  “Galton’s Problem in Cross-National Research.”  World Politics  29(1):1-28.

Brueckner, J.K.  2003.  “Strategic Interaction Among Governments: An Overview of Empirical Studies.”  International Regional Science Review 26(2): 175-188.

Simmons, Beth A., Frank Dobbin, and Geoffrey Garrett. 2006. “The International Diffusion of Liberalism.” International Organization 60(4):781-810.

Break

Diagnosing Spatial Dependence in OLS Residuals

References:

Anselin, Luc. 1995. “Local Indicators of Spatial Association – LISA.” Geographical Analysis 27: 93-115.

Buse, A. 1982. “The Likelihood Ratio, Wald, and LM Tests: An Expository Note.”  The American Statistician 36(3): 153-157.

Anselin, L., A. Bera, R.J. Florax, and M. Yoon. 1996.  “Simple Diagnostic Tests for Spatial Dependence.” Regional Science and Urban Economics, 26: 77-104.

2) Tuesday, December 13^th

Spatial Lag, Error, and Mixed Models I: A Typology of Structural Models

References:

Beck, N., K. Gleditsch, and K. Beardsley. 2006.  “Space is More than Geography: Using Spatial Econometrics in the Study of Political Economy.”  International Studies Quarterly 50: 27-44.

Neumayer, Eric and Thomas Plümper. 2009. “Spatial Effects in Dyadic Data.” International Organization 64(1).

Break

Spatial Lag, Error, and Mixed Models II: Estimation

References:

Doreian, Patrick. 1981.  “Estimating Linear Models with Spatially Distributed Data.” Sociological Methodology Vol. 12: 359-388.

Land, Kenneth C. and Glenn Deane. 1992.  “On the Large-Sample Estimation of Regression Models with Spatial or Network-Effects Terms: A Two-Stage Least Squares Approach.” Sociological Methodology, Vol. 22, pp. 221-248.

3) Wednesday, December 14^th

Spatial Lag, Error, and Mixed Models III: Calculating and Presenting Spatial Effects

References:

Franzese, R.J and J.C. Hays. 2007. “Spatial-Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15(2): 140-164.

Elhorst, J.P. 2001. “Dynamic Models in Space and Time.” Geographical Analysis 33:119-140.

Break

Spatiotemporal Models: Estimation & Interpretation

References:

Elhorst, J.P. 2001. “Dynamic Models in Space and Time.” Geographical Analysis 33:119-140.

Elhorst, J.P. 2010. “Spatial Panel-Data Models.” In M.M. Fischer & A. Getis, eds., Handbook of Applied Spatial Analysis. Berlin: Springer, pp. 377-407.

Franzese, R.J and J.C. Hays. 2007. “Spatial-Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15(2): 140-164.

Franzese, R., Hays, J. 2008. Empirical Models of Spatial Interdependence. In J. Box-Steffensmeier, H. Brady, D. Collier, eds., Oxford Handbook of Political Methodology, Oxford UP, pp. 570-604. (Use the hyperlinked version; it corrects an error in the printed version.)

4) Thursday, December 15^th

Limited Dependent Variables I: Spatial-Probit Model

References:

Beron, K. J., J.C. Murdoch, W.P. Vijverberg.  2003.  “Why Cooperate? Public Goods, Economic Power, and the Montreal Protocol.” Review of Economics and Statistics 85(2): 286-297.

Franzese, R., Hays, J. 2009. “The Spatial Probit Model of Interdependent Binary Outcomes: Estimation, Interpretation, and Presentation,” Public Choice Society Annual Meetings.

Break

Limited Dependent Variables II: Spatial-Duration and Spatial-Count Models

References:

Hays, J.C. 2009. “Bucking the System: Using Simulation Methods to Estimate and Analyze Systems of Equations with Qualitative and Limited Dependent Variables,” SLAMM (St. Louis Area Methods Meetings), Washington University in St. Louis.

Griffith, D.A. and R. Haining. 2006.  “Beyond Mule Kicks: The Poisson Distribution in Geographical Analysis.”  Geographical Analysis 38: 123-139.

Franzese, R., Hays, J. 2009. “A Comparison of the Small-Sample Properties of Several Estimators for Spatial-Lag Count-Models,” Political Methodology Society Annual Meetings.

Hays, J., Kachi, A. 2009. “Interdependent Duration Models in Political Science,” American Political Science Association Annual Meetings.

Darmofal, D. 2009. “Bayesian Spatial Survival Models for Political Event Processes,” American Journal of Political Science 53(1):241-57.

5) Friday, December 16^th

Multiparametric Spatial-Lag Models and Network-Behavior Coevolution

Reference:

Hays, J.C., Kachi, A., Franzese, R.J. 2010. “A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application,” Statistical Methodology 7(3): 406-28.

Break

Network and Spatial-Econometric Models of Network-Behavior Coevolution

Reference:

Franzese, R.J., Hays, J., Kachi, A., Franzese, R. 2010. “Modeling History Dependence in Network-Behavior Coevolution,” Political Analysis. Forthcoming.