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Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences

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The spatial autocorrelation issue is now well established, and it is almost impossible to deal with spatial data without considering this reality. In addition, recent developments have been devoted to developing methods that deal with spatial autocorrelation in panel data. However, little effort has been devoted to dealing with spatial data (cross-section) pooled over time. This paper endeavours to bridge the gap between the theoretical modeling development and the application based on spatial data pooled over time. The paper presents a schematic representation of how spatial links can be expressed, depending on the nature of the variable, when combining the spatial multidirectional relations and temporal unidirectional relations. After that, a Monte Carlo experiment is conducted to establish the impact of applying a usual spatial econometric model to spatial data pooled over time. The results suggest that neglecting the temporal dimension of the data generating process can introduce important biases on autoregressive parameters and thus result in the inaccurate measurement of the indirect and total spatial effect related to the spatial spillover effect.
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Dubé, J. and Legros, D. (2015) Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences. Theoretical Economics Letters, 5, 132-154. doi: 10.4236/tel.2015.51018. 
 

[1] Gosset, W.S. (1914) The Elimination of Spurious Correlation Due to Position in Time or Space. Biometrika, 10, 179-180.
[2] Anselin, L. and Bera, A.K. (1998) Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics. Statistics: Textbooks and Monographs, 155, 237-289.
[3] Griffith, D.A. (2005) Effective Geographic Sample Size in the Presence of Spatial Autocorrelation. Annals of the Association of American Geographers, 95, 740-760. http://dx.doi.org/10.1111/j.1467-8306.2005.00484.x
[4] LeSage, J. and Pace, K.R. (2009) Introduction to Spatial Econometrics. Chapman and Hall/CRC, London.
[5] LeSage, J.P. and Pace, R.K. (2004) Models for Spatially Dependent Missing Data. The Journal of Real Estate Finance and Economics, 29, 233-254. http://dx.doi.org/10.1023/B:REAL.0000035312.82241.e4
[6] Anselin, L. (2010) Thirty Years of Spatial Econometrics. Papers in Regional Science, 89, 3-25. http://dx.doi.org/10.1111/j.1435-5957.2010.00279.x
[7] Arbia, G. (2011) A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association. Spatial Economic Analysis, 6, 377-395.
[8] Dubé, J. and Legros, D. (2013) Dealing with Spatial Data Pooled over Time in Statistical Models. Letters in Spatial and Resource Sciences, 6, 1-18. http://dx.doi.org/10.1007/s12076-012-0082-3
[9] Dubé, J. and Legros, D. (2013) A Spatio-Temporal Measure of Spatial Dependence: An Example Using Real Estate Data. Papers in Regional Science, 92, 19-30.
[10] Huang, B., Huang, W. and Barry, M. (2010) Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices. International Journal of Geographical Information Science, 24, 383-401. http://dx.doi.org/10.1080/13658810802672469
[11] Smith, T. and Wu, P. (2009) A Spatio-Temporal Model of Housing Prices Based on Individual Sales Transactions over Time. Journal of Geographical Systems, 11, 333-355. http://dx.doi.org/10.1007/s10109-009-0085-9
[12] Moran, P. (1950) A Test for the Serial Independence of Residuals. Biometrika, 37, 178-181. http://dx.doi.org/10.1093/biomet/37.1-2.178
[13] Dubé, J., Thériault, M. and Des Rosiers, F. (2012) Using a Fourier Polynomial Expansion to Generate a Spatial Predictor. International Journal of Housing Market and Analysis, 5, 177-195. http://dx.doi.org/10.1108/17538271211225922
[14] Legendre, P. (1993) Spatial Autocorrelation: Trouble or New Paradigm? Ecology, 74, 1659-1673. http://dx.doi.org/10.2307/1939924
[15] Getis, A. and Aldstadt, J. (2004) Constructing the Spatial Weights Matrix Using a Local Statistic. Geographical Analysis, 36, 90-104. http://dx.doi.org/10.1111/j.1538-4632.2004.tb01127.x
[16] Haining, R. (2009) The Special Nature of Spatial Data. In: Fotheringham, A.S. and Rogerson, P.A., Eds., The Sage Handbook of Spatial Analysis, Sage, Thousand Oaks, 5-24.
[17] Pinkse, J. and Slade, M.E. (2010) The Future of Spatial Econometrics. Journal of Regional Science, 50, 103-117. http://dx.doi.org/10.1111/j.1467-9787.2009.00645.x
[18] McMillen, D.P. (2010) Issues in Spatial Data Analysis. Journal of Regional Science, 50, 119-141. http://dx.doi.org/10.1111/j.1467-9787.2009.00656.x
[19] Tobler, W. (1970) A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234-240. http://dx.doi.org/10.2307/143141
[20] McMillen, D.P. and McDonald, J.F. (2004) Locally Weighted Maximum-Likelihood Estimation: Monte Carlo Evidence and an Application. In: Anselin, L., Florax, R. and Rey, S.J., Eds., Advances in Spatial Econometrics: Methodology, Tools and Applications, Springer, Heidelberg, 225-240.
[21] LeSage, J. (2003) A Family of Geographically Weighted Regression Models. In: Anselin, L., Florax, R. and Rey, S.J., Eds., Advances in Spatial Econometrics: Methodology, Tools and Applications, Springer, Heidelberg, 241-278.
[22] Fotheringham, A.S., Brunsdon, C. and Charlton, M. (1998) Geographical Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environment and Planning A, 30, 1905-1927. http://dx.doi.org/10.1068/a301905
[23] Fotheringham, A.S., Brunsdon, C. and Charlton, M. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Hoboken.
[24] Hégerstrand, T. (1970) What about People in Regional Science? Papers of the Regional Science Association, 24, 7-21.
[25] Dubé, J., Baumont, C. and Legros, D. (2011) Utilisation des matrices de pondérations en économétrie spatiale: Proposition dans un contexte spatio-temporel. Discussion Paper e2011-01, Laboratoire d’ économie et de Gestion, Université de Bourgogne, Dijon.
[26] Pace, K.R., Barry, R., Clapp, J.M. and Rodriguez, M. (1998) Spatiotemporal Autoregressive Models of Neighborhood Effects. Journal of Real Estate Finance and Economics, 17, 15-33. http://dx.doi.org/10.1023/A:1007799028599
[27] Pace, K.R., Barry, R., Gilley, W.O. and Sirmans, C.F. (2000) A Method for Spatial-Temporal Forecasting with an Application to Real Estate Prices. International Journal of Forecasting, 16, 229-246. http://dx.doi.org/10.1016/S0169-2070(99)00047-3
[28] Wooldridge, J.M. (2001) Econometric Analysis of Cross Section and Panel Data. The MIT Press, Cambridge.
[29] LeSage, J. (1999) Spatial Econometrics. The Web Book of Regional Science, Regional Research Institute, West Virginia University, Morgantown.
[30] Des Rosiers, F., Dubé, J. and Thériault, M. (2011) Do Peer Effects Shape Property Values? Journal of Property Investment & Finance, 29, 510-528. http://dx.doi.org/10.1108/14635781111150376
[31] Dubé, J. and Legros, D. (2014) Spatial Data and Econometrics. Wiley-ISTE, Hoboken.
[32] LeSage, J. and Fischer, M. (2009) Spatial Growth Regressions: Model Specification, Estimation and Interpretation. Spatial Economic Analysis, 3, 275-304. http://dx.doi.org/10.1080/17421770802353758   eww150225lx
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