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Spatial Panel Data Models

  • J. Paul ElhorstEmail author
Chapter

Abstract

In recent years, the spatial econometrics literature has exhibited a growing interest in the specification and estimation of econometric relationships based on spatial panels. Spatial panels typically refer to data containing time series observations of a number of spatial units (zip codes, municipalities, regions, states, jurisdictions, countries, etc.). This interest can be explained by the fact that panel data offer researchers extended modeling possibilities as compared to the single equation cross-sectional setting, which was the primary focus of the spatial econometrics literature for a long time. Panel data are generally more informative, and they contain more variation and less collinearity among the variables. The use of panel data results in a greater availability of degrees of freedom, and hence increases efficiency in the estimation. Panel data also allow for the specification of more complicated behavioral hypotheses, including effects that cannot be addressed using pure cross-sectional data (see Hsiao 2005 for more details).

Keywords

Panel Data Model Spatial Weight Matrix Spatial Error Model Spatial Econometric Spatial Panel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Allers MA, Elhorst JP (2005) Tax mimicking and yardstick competition among governments in the Netherlands. Int Tax Publ Fin 12(4):493–513CrossRefGoogle Scholar
  2. Anselin L (1988) Spatial econometrics: methods and models. Kluwer, DordrechtGoogle Scholar
  3. Anselin L, Bera AK (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. In Ullah A, Giles DEA (eds) Handbook of applied economic statistics. Marcel Dekker, New York, pp. 237–289Google Scholar
  4. Anselin L, Hudak S (1992) Spatial econometrics in practice: a review of software options. Reg Sci Urban Econ 22(3):509–536CrossRefGoogle Scholar
  5. Anselin L, Le Gallo J, Jayet H (2006) Spatial panel econometrics. In Matyas L, Sevestre P. (eds) The econometrics of panel data, fundamentals and recent developments in theory and practice (3rd edition). Kluwer, Dordrecht, pp. 901–969Google Scholar
  6. Anselin L, Bera AK, Florax R, Yoon MJ (1996) Simple diagnostic tests for spatial dependence. Reg Sci Urban Econ 26(1):77–104CrossRefGoogle Scholar
  7. Baltagi BH (1989) Applications of a necessary and sufficient condition for OLS to be BLUE. Stat Prob Letters 8(5):457–461CrossRefGoogle Scholar
  8. Baltagi BH (2005) Econometric analysis of panel data (3rd edition). Wiley, New York, Chichester, Toronto and BrisbaneGoogle Scholar
  9. Baltagi BH (2006) Random effects and spatial autocorrelation with equal weights. Econ Theory 22(5):973–984CrossRefGoogle Scholar
  10. Baltagi BH, Li D (2004) Prediction in the panel data model with spatial autocorrelation. In Anselin L, Florax RJGM, Rey SJ (eds) Advances in spatial econometrics: methodology, tools, and applications. Springer, Berlin, Heidelberg and New York, pp. 283–295Google Scholar
  11. Baltagi BH, Song SH, Jung BC, Koh W (2007) Testing for serial correlation, spatial autocorrelation and random effects using panel data. J Econometrics 140(1):5–51CrossRefGoogle Scholar
  12. Beck N (2001) Time-series-cross-section data: what have we learned in the past few years? Ann Rev Pol Sci 4(1):271–293CrossRefGoogle Scholar
  13. Breusch TS (1987) Maximum likelihood estimation of random effects models. J Econometrics 36(3):383–389CrossRefGoogle Scholar
  14. Brueckner JK (2003) Strategic interaction among local governments: an overview of empirical studies. Int Reg Sci Rev 26(2):175–188CrossRefGoogle Scholar
  15. Cressie NAC (1993) Statistics for spatial data (revised edition). Wiley, New York, Chichester, Toronto and BrisbaneGoogle Scholar
  16. Elhorst JP (2001) Dynamic models in space and time. Geogr Anal 33(2):119–140Google Scholar
  17. Elhorst JP (2003) Specification and estimation of spatial panel data models. Int Reg Sci Rev 26(3):244–268CrossRefGoogle Scholar
  18. Elhorst JP (2005a) Unconditional maximum likelihood estimation of linear and log-linear dynamic models for spatial panels. Geogr Anal 37(1):62–83CrossRefGoogle Scholar
  19. Elhorst J.P. (2005b) Models for dynamic panels in space and time; an application to regional unemployment in the EU. Paper presented at the Spatial Econometrics Workshop, April 8–9, 2005, KielGoogle Scholar
  20. Elhorst JP (2008a) A spatiotemporal analysis of aggregate labour force behaviour by sex and age across the European Union. J Geogr Syst 10(2):167–190CrossRefGoogle Scholar
  21. Elhorst JP (2008b) Serial and spatial autocorrelation. Econ Letters 100(3):422–424CrossRefGoogle Scholar
  22. Elhorst JP, Freret S (2009) Evidence of political yardstick competition in France using a two-regime spatial Dublin model with fixed effects. J Reg Sci. DOI: 10.1111/j.1467-9787.2009.00613.x [forthcoming]Google Scholar
  23. Elhorst JP, Blien U, Wolf K (2007) New evidence on the wage curve: a spatial panel approach. Int Reg Sci Rev 30(2):173–191CrossRefGoogle Scholar
  24. Elhorst JP, Piras G, Arbia G (2006) Growth and convergence in a multi-regional model with space-time dynamics. Paper presented at the Spatial Econometric Workshop, May 25–27, 2006, RomeGoogle Scholar
  25. Ertur C, Koch W (2007) Growth, technological interdependence and spatial externalities: theory and evidence. J Appl Econ 22(6):1033–1062CrossRefGoogle Scholar
  26. Fingleton B (2008) A generalized method of moments estimator for a spatial panel data model with endogenous spatial lag and spatial moving average errors. Spat Econ Anal 3(1):27–44CrossRefGoogle Scholar
  27. Fingleton B, Le Gallo J (2007) Estimating spatial models with endogenous variables, a spatial lag en spatially dependent disturbances: finite sample properties. Paper presented at the First World Conference of the Spatial Econometrics Association, July 11–14, 2007, CambridgeGoogle Scholar
  28. Florax RJGM, Folmer H (1992) Specification and estimation of spatial linear regression models. Reg Sci Urban Econ 22(3):405–432CrossRefGoogle Scholar
  29. Florax RJGM, Folmer H, Rey SJ (2003) Specification searches in spatial econometrics: the relevance of Hendry's methodology. Reg Sci Urban Econ 33(5):557–579CrossRefGoogle Scholar
  30. FranzeseJr RJ, Hays JC (2007) Spatial econometric models of cross-sectional interdependence in political science panel and time-series-cross-section data. Pol Anal 15(2):140–164CrossRefGoogle Scholar
  31. Goldberger AS (1962) Best linear unbiased prediction in the generalized linear regression model. J Am Stat Assoc 57:369–375CrossRefGoogle Scholar
  32. Greene WH (2008) Econometric analysis (6th edition). Pearson, Upper Saddle River [NJ]Google Scholar
  33. Griffith DA (1988) Advanced spatial statistics. Kluwer, DordrechtGoogle Scholar
  34. Griffith DA, Lagona F (1998) On the quality of likelihood-based estimators in spatial auto-regressive models when the data dependence structure is mis-specified. J Stat Plann Inference 69(1):153–174CrossRefGoogle Scholar
  35. Hendry DF (2006) A comment on ‘Specification searches in spatial econometrics: The relevance of Hendry's methodology’. Reg Sci Urban Econ 36(2):309–312CrossRefGoogle Scholar
  36. Hsiao C (2003) Analysis of Panel Data (2nd edition). Cambridge University Press, CambridgeGoogle Scholar
  37. Hsiao C (2005) Why panel data? University of Southern California, IEPR Working Paper 05.33Google Scholar
  38. Hunneman A, Bijmolt T, Elhorst JP (2007) Store location evaluation based on geographical consumer information. Paper presented at the Marketing Science Conference, June 28–30, 2007, SingaporeGoogle Scholar
  39. Jarque CM, Bera AK (1980) Efficient tests for normality, homoskedasticity and serial independence of regression residuals. Econ Letters 6(3):255–259CrossRefGoogle Scholar
  40. Kapoor M, Kelejian HH Prucha IR (2007) Panel data models with spatially correlated error components. J Econometrics 140(1):97–130CrossRefGoogle Scholar
  41. Kelejian HH, Prucha IR (1998) A generalized spatial two stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. J Real Est Fin Econ 17(1):99–121CrossRefGoogle Scholar
  42. Kelejian HH, Prucha IR, Yuzefovich Y (2006) Estimation problems in models with spatial weighting matrices which have blocks of equal elements. J Reg Sci 46(3):507–515CrossRefGoogle Scholar
  43. Kholodilin KA, Siliverstovs B, Kooths S (2008) A dynamic panel data approach to the forecasting of the GDP of German Länder. Spat Econ Anal 3(2):195–207CrossRefGoogle Scholar
  44. Korniotis GM (2005) A dynamic panel estimator with both fixed and spatial effects. Paper presented at the Spatial Econometrics Workshop, April 8–9, 2005, KielGoogle Scholar
  45. Lahiri SN (2003) Central limit theorems for weighted sums of a spatial process under a class of stochastic and fixed designs. Sankhya 65(2): 356–388Google Scholar
  46. Lee LF (2003) Best spatial two-stage least squares estimators for a spatial autoregressive model with autoregressive disturbances. Econ Rev 22(4):307–335CrossRefGoogle Scholar
  47. Lee LF (2004) Asymptotic distribution of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica 72(6):1899–1925CrossRefGoogle Scholar
  48. Leenders RTAJ (2002) Modeling social influence through network autocorrelation: Constructing the weight matrix. Soc Netw 24(1):21–47CrossRefGoogle Scholar
  49. LeSage JP (1999) Spatial econometrics. www.spatial-econometrics.com/html/sbook.pdf
  50. LeSage JP, Pace RK (2007) A matrix exponential spatial specification. J Econometrics 140(1):190–214CrossRefGoogle Scholar
  51. Magnus JR (1982) Multivariate error components analysis of linear and non-linear regression models by maximum likelihood. J Econometrics 19(2):239–285CrossRefGoogle Scholar
  52. Magnus JR, Neudecker H (1988) Matrix differential calculus with applications in statistics and econometrics. Wiley, New York, Chichester, Toronto and BrisbaneGoogle Scholar
  53. Manski CF (1993) Identification of endogenous social effects:the reflection problem. Rev Econ Stud 60:531–542CrossRefGoogle Scholar
  54. Mood AM, Graybill F, Boes DC (1974) Introduction to the theory of statistics (3rd edition). McGraw-Hill, TokyoGoogle Scholar
  55. Nerlove M, Balestra P (1996) Formulation and estimation of econometric models for panel data. In Mátyás L, Sevestre P (eds) The econometrics of panel data (2nd edition). Kluwer, Dordrecht, pp. 3–22Google Scholar
  56. Ord JK (1975) Estimation methods for models of spatial interaction. J Am Stat Assoc 70:120–126CrossRefGoogle Scholar
  57. Pace RK, Barry R (1997) Quick computation of spatial autoregressive estimators. Geogr Anal 29(3):232–246Google Scholar
  58. Partridge MD (2005) Does income distribution affect U.S. state economic growth. J Reg Sci 45(2):363–394CrossRefGoogle Scholar
  59. Su L, Yang Z (2007) QML Estimation of dynamic panel fata models with spatial errors. Paper presented at the First World Conference of the Spatial Econometrics Association, July 11–14, 2007, CambridgeGoogle Scholar
  60. Verbeek M (2000) A guide to modern econometrics. Wiley, New York, Chichester, Toronto and BrisbaneGoogle Scholar
  61. Vrijburg H, Jacobs JPAM, Ligthart JE (2007) A spatial econometric approach to commodity tax competition. Paper presented at the NAKE Research Day, October 24, 2007, UtrechtGoogle Scholar
  62. Yang Z, Li C, Tse YK (2006) Functional form and spatial dependence in spatial panels. Econ Letters 91(1):138–145CrossRefGoogle Scholar
  63. Yu J, Jong R de, Lee L (2007) Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large. J Econometrics 146(1):118–134CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Economics and EconometricsUniversity of GroningenGroningenThe Netherlands

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