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Spatial Panel Econometrics

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The Econometrics of Panel Data

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references

  • Anselin, L. (1988a). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht, The Netherlands.

    Google Scholar 

  • Anselin, L. (1988b). A test for spatial autocorrelation in seemingly unrelated regressions. Economics Letters, 28:335–341.

    Google Scholar 

  • Anselin, L. (1990).Some robust approaches to testing and estimation in spatial econometrics.Regional Science and Urban Economics, 20:141–163.

    Google Scholar 

  • Anselin, L. (2001a). Rao’s score test in spatial econometrics. Journal of Statistical Planning and Inference, 97:113–139.

    Google Scholar 

  • Anselin, L. (2001b). Spatial econometrics. In Baltagi, Badi, editor, A Companion to Theoretical Econometrics, pages 310–330. Blackwell, Oxford.

    Google Scholar 

  • Anselin, L. (2002). Under the hood. Issues in the specification and interpretation of spatial regression models.Agricultural Economics, 27(3):247–267.

    Google Scholar 

  • Anselin, L. (2003). Spatial externalities, spatial multipliers and spatial econometrics. International Regional Science Review, 26(2):153–166.

    Google Scholar 

  • Anselin, L. and Bera, A. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics.In Ullah, Amman and Giles, David E.A., editors, Handbook of Applied Economic Statistics, pages 237–289. Marcel Dekker, New York.

    Google Scholar 

  • Anselin, L., Bera, A., Florax, Raymond J.G.M., and Yoon, M. (1996).Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26:77–104.

    Article  Google Scholar 

  • Anselin, L. and Florax, Raymond J.G.M. (1995). New Directions in Spatial Econometrics. Springer-Verlag, Berlin.

    Google Scholar 

  • Anselin, L., Florax, Raymond J.G.M., and Rey, Sergio J. (2004). Econometrics for spatial models, recent advances. In Anselin, Luc, Florax, Raymond J.G.M., and Rey, Sergio J., editors,Advances in Spatial Econometrics. Methodology, Tools and Applications,pages 1–25. Springer-Verlag, Berlin.

    Google Scholar 

  • Anselin, L. and Le Gallo, J. (2004). Panel Data Spatial Econometrics with PySpace. Spatial Analysis Laboratory (SAL). Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, IL.

    Google Scholar 

  • Anselin, L. and Moreno, R. (2003). Properties of tests for spatial error components.Regional Science and Urban Economics, 33(5):595–618.

    Article  Google Scholar 

  • Anselin, L., Syabri, I., and Kho, Y. (2006). Geoda, an introduction to spatial data analysis. Geographical Analysis. 38(1):5–22.

    Google Scholar 

  • Arellano, M. (2003). Panel Data Econometrics. Oxford University Press, Oxford, United Kingdom.

    Google Scholar 

  • Baltagi, Badi H. (2001). Econometric Analysis of Panel Data (Second Edition). John Wiley & Sons, Chichester, United Kingdom.

    Google Scholar 

  • Baltagi, Badi H., Egger, P., and Pfaffermayr, M. (2006). A generalized spatial panel data model with random effects. Working paper, Syracuse University, Syracuse, NY.

    Google Scholar 

  • Baltagi, Badi H., Song, Seuck H., Jung, Byoung C., and Koh, W. (2007). Testing for serial correlation, spatial autocorrelation and random effects using panel data.Journal of Econometrics, 140(1):5–51.

    Article  Google Scholar 

  • Baltagi, Badi H., Song, Seuck H., and Koh, W. (2003). Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117:123–150.

    Article  Google Scholar 

  • Banerjee, S., Carlin, Bradley P., and Gelfand, Alan E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC, Boca Raton, FL.

    Google Scholar 

  • Barry, Ronald P. and Pace, R. Kelley (1999). Monte Carlo estimates of the log determinant of large sparse matrices.Linear Algebra and its Applications, 289:41–54.

    Article  Google Scholar 

  • Bivand, R. (2002).Spatial econometrics functions in R: Classes and methods.Journal of Geographical Systems, 4:405–421.

    Google Scholar 

  • Brock, William A. and Durlauf, Steven N. (2001). Discrete choice with social interactions. Review of Economic Studies, 68(2):235–260.

    Article  Google Scholar 

  • Brueckner, Jan K. (2003). Strategic interaction among governments: An overview of empirical studies.International Regional Science Review, 26(2):175–188.

    Article  Google Scholar 

  • Burridge, P. (1980). On the Cliff-Ord test for spatial autocorrelation. Journal of the Royal Statistical Society B, 42:107–108.

    Google Scholar 

  • Case, Anne C. (1991). Spatial patterns in household demand. Econometrica, 59:953–965.

    Google Scholar 

  • Case, Anne C. (1992). Neighborhood influence and technological change. Regional Science and Urban Economics, 22:491–508.

    Google Scholar 

  • Case, Anne C., Rosen, Harvey S., and Hines, James R. (1993). Budget spillovers and fiscal policy interdependence: Evidence from the states.Journal of Public Economics, 52:285–307.

    Article  Google Scholar 

  • Casetti, E. (1997). The expansion method, mathematical modeling, and spatial econometrics.International Regional Science Review, 20:9–33.

    Google Scholar 

  • Chen, X. and Conley, Timothy G. (2001). A new semiparametric spatial model for panel time series. Journal of Econometrics, 105:59–83.

    Article  Google Scholar 

  • Cliff, A. and Ord, J. Keith (1981). Spatial Processes: Models and Applications. Pion, London.

    Google Scholar 

  • Coakley, J., Fuentes, A.-M., and Smith, R. (2002). A principal components approach to cross-section dependence in panels.Working Paper, Department of Economics, Birkbeck College, University of London, London, United Kingdom.

    Google Scholar 

  • Conley, Timothy G. (1999). GMM estimation with cross-sectional dependence. Journal of Econometrics, 92:1–45.

    Article  Google Scholar 

  • Conley, Timothy G. and Ligon, E. (2002). Economic distance, spillovers and cross country comparisons. Journal of Economic Growth, 7:157–187.

    Article  Google Scholar 

  • Conley, Timothy G. and Topa, G. (2002). Socio-economic distance and spatial patterns in unemployment. Journal of Applied Econometrics, 17:303–327.

    Article  Google Scholar 

  • Cressie, N. and Huang, H.-C. (1999). Classes of nonseparable spatio-temporal stationary covariance functions.Journal of the American Statistical Association, 94:1330–1340.

    Article  Google Scholar 

  • Cressie, N. (1993).Statistics for Spatial Data. Wiley, New York.

    Google Scholar 

  • Driscoll, John C. and Kraay, Aart C. (1998). Consistent covariance matrix estimation with spatially dependent panel data.The Review of Economics and Statistics, 80:549–560.

    Article  Google Scholar 

  • Druska, V. and Horrace, William C. (2004). Generalized moments estimation for spatial panel data: Indonesian rice farming.American Journal of Agricultural Economics, 86(1):185–198.

    Article  Google Scholar 

  • Dubin, R. (1988). Estimation of regression coefficients in the presence of spatially autocorrelated errors.Review of Economics and Statistics, 70:466–474.

    Google Scholar 

  • Dubin, R. (1995). Estimating logit models with spatial dependence. In Anselin, Luc and Florax, Raymond J.G.M., editors, New Directions in Spatial Econometrics, pages 229–242. Springer-Verlag, Berlin.

    Google Scholar 

  • , Elhorst, J. Paul (2001). Dynamic models in space and time. Geographical Analysis, 33:119–140.

    Google Scholar 

  • Elhorst, J. Paul (2003). Specification and estimation of spatial panel data models. International Regional Science Review, 26(3):244–268.

    Google Scholar 

  • Fazekas, I., Florax, R., and Folmer, H. (1994). On maximum likelihood estimators of parameters of spatio-temporal econometric models.Technical Report No. 109/1994, Kossuth University, Debrecen,Hungary.

    Google Scholar 

  • Florax, Raymond J.G.M. and Van Der Vlist, Arno J. (2003). Spatial econometric data analysis: Moving beyond traditional models. International Regional Science Review, 26(3):223–243.

    Google Scholar 

  • Fotheringham, A. Stewart, Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression. John Wiley, Chichester.

    Google Scholar 

  • Gamerman, D., Moreira, Ajax R.B., and Rue, H. (2003). Space-varying regression models: Specifications and simulation. Computational Statistics & Data Analysis, 42(3):513–533.

    Article  Google Scholar 

  • Gelfand, Alan E., Kim, H.-J., Sirmans, C.F., and Banerjee, S. (2003). Spatial modeling with spatially varying coefficient processes. Journal of the American Statistical Association, 98:387–396.

    Article  Google Scholar 

  • Giacomini, R. and Granger, Clive W.J. (2004). Aggregation of space-time processes. Journal of Econometrics, 118:7–26.

    Article  Google Scholar 

  • Glaeser, Edward L., Sacerdote, Bruce I., and Scheinkman, Jose A. (2002).The social multiplier. Technical Report 9153, NBER, Cambridge, MA 02138.

    Google Scholar 

  • Haining, R. (1990). Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, Cambridge.

    Google Scholar 

  • Hsiao, C. (1986). Analysis of Panel Data. Cambridge University Press, Cambridge.

    Google Scholar 

  • Hsiao, C. and Pesaran, M. Hashem (2008). Random coefficient panel data models. In Matyas L. and Sevestre P., editors, The Econometrics of Panel Data. Kuwer Academic Publishers, Dordrecht.

    Google Scholar 

  • Hsiao, C., Pesaran, M. Hashem, and Tahmiscioglu, A. Kamil (2002). Maximum likelihood estimation of fixed effects dynamic panel models covering short time periods.Journal of Econometrics, 109:107–150.

    Article  Google Scholar 

  • Kapoor, M., Kelejian, Harry H., and Prucha, Ingmar R. (2007). Panel data models with spatially correlated error components. Journal of Econometrics, 140(1):97–130.

    Article  Google Scholar 

  • Kelejian, Harry H. and Prucha, I. (1998). A generalized spatial two stage least squares procedures for estimating a spatial autoregressive model with autoregressive disturbances. Journal of Real Estate Finance and Economics, 17:99–121.

    Article  Google Scholar 

  • Kelejian, Harry H. and Prucha, I. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model.International Economic Review, 40:509–533.

    Article  Google Scholar 

  • Kelejian, Harry H. and Robinson, Dennis P. (1993). A suggested method of estimation for spatial interdependent models with autocorrelated errors, and an application to a county expenditure model. Papers in Regional Science, 72:297–312.

    Google Scholar 

  • , Kelejian, Harry H. and Robinson, Dennis P. (1995). Spatial correlation: A suggested alternative to the autoregressive model.In Anselin, Luc and Florax, Raymond J.G.M., editors, New Directions in Spatial Econometrics, pages 75–95. Springer-Verlag, Berlin.

    Google Scholar 

  • Kelejian, Harry H. and Robinson, Dennis P. (1998). A suggested test for spatial autocorrelation and/or heteroskedasticity and corresponding Monte Carlo results. Regional Science and Urban Economics, 28:389–417.

    Article  Google Scholar 

  • Lee, L.-F. (2002). Consistency and efficiency of least squares estimation for mixed regressive, spatial autoregressive models.Econometric Theory, 18(2):252–277.

    Google Scholar 

  • Lee, L.-F. (2003). Best spatial two-stage least squares estimators for a spatial autoregressive model with autoregressive disturbances. Econometric Reviews, 22:307–335.

    Google Scholar 

  • Magnus, J. (1978). Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix.Journal of Econometrics, 7:281–312.Corrigenda, Journal of Econometrics 10, 261.

    Google Scholar 

  • Manski, Charles F. (1993). Identification of endogenous social effects: The reflexion problem. Review of Economic Studies, 60:531–542.

    Article  Google Scholar 

  • Manski, Charles F. (2000). Economic analysis of social interactions. Journal of Economic Perspectives, 14(3):115–136.

    Article  Google Scholar 

  • Mardia, K.V. and Goodall, C. (1993). Spatio-temporal analyses of multivariate environmental monitoring data. In Patil, G.P. and Rao, C.R., editors, Multivariate Environmental Statistics, pages 347–386. Elsevier, Amsterdam.

    Google Scholar 

  • Mardia, K.V. and Marshall, R.J. (1984). Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika, 71:135–146.

    Article  Google Scholar 

  • Ord, J. Keith (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association, 70:120–126.

    Google Scholar 

  • Pace, R. Kelley and Barry, R. (1997). Sparse spatial autoregressions. Statistics and Probability Letters, 33:291–297.

    Article  Google Scholar 

  • Paelinck, J. and Klaassen, L. (1979). Spatial Econometrics. Saxon House, Farnborough.

    Google Scholar 

  • Pesaran, M. Hashem (2002). Estimation and inference in large heterogenous panels with cross section dependence. DAE Working Paper 0305 and CESifo Working Paper no. 869, University of Cambridge, Cambridge, United Kingdom.

    Google Scholar 

  • Pesaran, M. Hashem (2004). General diagnostic tests for cross section dependence in panels. Working paper, University of Cambridge, Cambridge, United Kingdom.

    Google Scholar 

  • Rey, Sergio J. and Montouri, Brett D. (1999). US regional income convergence: A spatial econometrics perspective. Regional Studies, 33:143–156.

    Article  Google Scholar 

  • Smirnov, O. and Anselin, L. (2001). Fast maximum likelihood estimation of very large spatial autoregressive models: A characteristic polynomial approach. Computational Statistics and Data Analysis, 35:301–319.

    Article  Google Scholar 

  • Stein, Michael L. (1999). Interpolation of Spatial Data, Some Theory for Kriging. Springer-Verlag, New York.

    Google Scholar 

  • Topa, G. (2001). Social interactions, local spillover and unemployment. Review of Economic Studies, 68(2):261–295.

    Google Scholar 

  • Upton, Graham J. and Fingleton, B. (1985). Spatial Data Analysis by Example. Vol. 1: Point Pattern and Quantitative Data. Wiley, New York.

    Google Scholar 

  • Waller, L., Carlin, B., and Xia, H. (1997a). Structuring correlation within hierarchical spatio-temporal models for disease rates. In Grègoire, T., Brillinger, D., Russek-Cohen, P., Warren, W., and Wolfinger, R., editors, Modeling Longitudinal and Spatially Correlated Data, pages 309–319. Springer-Verlag, New York.

    Google Scholar 

  • Waller, L., Carlin, B., Xia, H., and Gelfand, A. (1997b). Hierarchical spatio-temporal mapping of disease rates. Journal of the American Statistical Association, 92:607–617.

    Article  Google Scholar 

  • Wikle, Christopher K., Berliner, L. Mark, and Cressie, N. (1998). Hierarchical Bayesian space-time models. Environmental and Ecological Statistics, 5:117–154.

    Article  Google Scholar 

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Anselin, L., Gallo, J.L., Jayet, H. (2008). Spatial Panel Econometrics. In: Mátyás, L., Sevestre, P. (eds) The Econometrics of Panel Data. Advanced Studies in Theoretical and Applied Econometrics, vol 46. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75892-1_19

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