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Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation

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Listed author(s):
  • José-Antonio Monteiro
  • Madina Kukenova

Abstract

This paper investigates the finite sample properties of estimators for spatial dynamic panel models in the presence of several endogenous variables. So far, none of the available estimators in spatial econometrics allows considering spatial dynamic models with one or more endogenous variables. We propose to apply system-GMM, since it can correct for the endogeneity of the dependent variable, the spatial lag as well as other potentially endogenous variables using internal and/or external instruments. The Monte-Carlo investigation compares the performance of spatial MLE, spatial dynamic MLE (Elhorst (2005)), spatial dynamic QMLE (Yu et al. (2008)), LSDV, difference-GMM (Arellano & Bond (1991)), as well as extended-GMM (Arellano & Bover (1995), Blundell & Bover (1998)) in terms of bias, root mean squared error and standard-error accuracy. The results suggest that, in order to account for the endogeneity of several covariates, spatial dynamic panel models should be estimated using extended GMM. On a practical ground, this is also important, because system-GMM avoids the inversion of high dimension spatial weights matrices, which can be computationally unfeasible for large N and/or T.

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File URL: ftp://sitelftp.unine.ch/RePEc/irn/pdfs/WP09-01.pdf
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Paper provided by IRENE Institute of Economic Research in its series IRENE Working Papers with number 09-01.

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Length: 29 pages
Date of creation: Mar 2009
Handle: RePEc:irn:wpaper:09-01
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Keywords: Spatial Econometrics; Dynamic Panel Model; System GMM; Monte Carlo Simulations;

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Find related papers by JEL classification:
  • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
  • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

References listed on IDEAS
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  1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
  2. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
  3. Elhorst, J. Paul, 2010. "Dynamic panels with endogenous interaction effects when T is small," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 272-282, September.
  4. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
  5. Elhorst, J. Paul, 2001. "Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable," Research Report 01C05, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  6. Michael Beenstock & Daniel Felsenstein, 2007. "Spatial Vector Autoregressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(2), pages 167-196.
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  11. Franzese, Robert J. & Hays, Jude C., 2007. "Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data," Political Analysis, Cambridge University Press, vol. 15(02), pages 140-164, March.
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  15. Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2007. "Estimating models of complex FDI: Are there third-country effects?," Journal of Econometrics, Elsevier, vol. 140(1), pages 260-281, September.
  16. Peter Phillips & Hyungsik Moon, 2000. "Nonstationary panel data analysis: an overview of some recent developments," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 263-286.
  17. Bruce A. Blonigen & Ronald B. Davies & Helen T. Naughton & Glen R. Waddell, 2005. "Spacey Parents: Spatial Autoregressive Patterns in Inbound FDI," NBER Working Papers 11466, National Bureau of Economic Research, Inc.
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  19. repec:dgr:rugsom:01c05 is not listed on IDEAS
  20. Kukenova, Madina & Monteiro, Jose-Antonio, 2008. "Does Lax Environmental Regulation Attract FDI when accounting for "third-country" effects?," MPRA Paper 11321, University Library of Munich, Germany, revised Sep 2008.
  21. Keller, Wolfgang & Shiue, Carol H., 2007. "The origin of spatial interaction," Journal of Econometrics, Elsevier, vol. 140(1), pages 304-332, September.
  22. Blonigen, Bruce A. & Davies, Ronald B. & Waddell, Glen R. & Naughton, Helen T., 2007. "FDI in space: Spatial autoregressive relationships in foreign direct investment," European Economic Review, Elsevier, vol. 51(5), pages 1303-1325, July.
  23. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
  24. Elhorst, J. Paul, 2003. "Unconditional maximum likelihood estimation of dynamic models for spatial panels," Research Report 03C27, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  25. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
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