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Dynamic Panel Analysis under Cross-Sectional Dependence

  • Khusrav Gaibulloev (a1), Todd Sandler (a2) and Donggyu Sul (a3)

Abstract

This article investigates inconsistency and invalid statistical inference that often characterize dynamic panel analysis in international political economy. These econometric concerns are tied to Nickell bias and cross-sectional dependence. First, we discuss how to avoid Nickell bias in dynamic panels. Second, we put forward factor-augmented dynamic panel regression as a means for addressing cross-sectional dependence. As a specific application, we use our methods for an analysis of the impact of terrorism on economic growth. Different terrorism variables are shown to have no influence on economic growth for five regional samples when Nickell bias and cross-dependence are taken into account. Our finding about terrorism and growth is contrary to the extant literature.

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Copyright

This is an Open-Access article, distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

e-mail: tsandler@utdallas.edu (corresponding author)

Footnotes

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Authors' note: We have profited from comments provided by two anonymous reviewers and R. Michael Alvarez. Any opinions, findings, conclusions, or recommendations are solely those of the authors, and do not necessarily reflect the views of DHS or CREATE. Replication materials for this article are available from the Political Analysis dataverse at http://hdl.handle.net/1902.1/22448. Supplementary materials for this article are available on the Political Analysis Web site.

Footnotes

References

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Alvarez, J., and Arellano, M. 2003. The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71: 1121–59.
Arellano, M. 1993. On the testing of correlated effects with panel data. Journal of Econometrics 59: 8797.
Bafumi, J., and Gelman, A. 2006. Fitting multilevel models when predictors and group effects correlate. Working paper, www.stat.columbia.edu/∼gelman/research/unpublished/Bafumi_Gelman_Midwest06.pdf (accessed May 26, 2013).
Bai, J. 2009. Panel data models with interactive fixed effects. Econometrica 77: 1229–79.
Beck, N., and Katz, J. N. 2011. Modeling dynamics in time-series-cross-section political economy data. Annual Review of Political Science 14: 331–52.
Blomberg, S. B., Broussard, N. H., and Hess, G. D. 2011. New wine in old wineskins? Growth, terrorism and the resource curse in sub-Saharan Africa. European Journal of Political Economy 27: S50S63.
Blomberg, S. B., Hess, G. D., and Orphanides, A. 2004. The macroeconomic consequences of terrorism. Journal of Monetary Economics 51: 1007–32.
Brinks, D., and Coppedge, M. 2006. Diffusion is no illusion: Neighbor emulation in the third wave of democracy. Comparative Political Studies 39: 463–89.
Chambers, D., and Guo, J.-T. 2009. Natural resources and economic growth: Some theory and evidence. Annals of Economics and Finance 10: 367–89.
Christopoulos, D. K., and Tsionas, E. G. 2004. Financial development and economic growth: Evidence from panel unit root and cointegration tests. Journal of Development Economics 73: 5574.
Cieślik, A., and Tarsalewska, M. 2011. External openness and economic growth in developing countries. Review of Development Economics 15: 729–44.
Enders, W., and Sandler, T. 2005. After 9/11: Is it all different now? Journal of Conflict Resolution 49: 259–77.
Enders, W., Sandler, T., and Gaibulloev, K. 2011. Domestic versus transnational terrorism: Data, decomposition, and dynamic. Journal of Peace Research 48: 319–37.
Engene, J. O. 2007. Five decades of terrorism in Europe: The TWEED dataset. Journal of Peace Research 44: 109–21.
Gaibulloev, K., and Sandler, T. 2008. Growth consequences of terrorism in Western Europe. Kyklos 61: 411–24.
Gaibulloev, K., and Sandler, T. 2009. The impact of terrorism and conflicts on growth in Asia. Economics & Politics 21: 359–83.
Gaibulloev, K., and Sandler, T. 2011. The adverse effect of transnational and domestic terrorism on growth in Africa. Journal of Peace Research 48: 355–71.
Gaibulloev, K., Sandler, T., and Sul, D. 2013. Common drivers of transnational terrorism: Principal component analysis. Economic Inquiry 51: 707–21.
Gaibulloev, K., Sandler, T., and Sul, D. 2014. Replication data for: Dynamic panel analysis under cross-sectional dependence. http://hdl.handle.net/1902.1/22448. IQSS Dataverse Network [Distributor] VI [Version] (accessed November 13, 2013).
Garrett, G., and Mitchell, D. 2001. Globalization, government spending and taxation in the OECD. European Journal of Political Research 39: 145–77.
Green, D. P., Kim, S. Y., and Yoon, D. H. 2001. Dirty pool. International Organization 55: 441–68.
Greenaway-McGrevy, R., Han, C., and Sul, D. 2012. Asymptotic distribution of factor augmented estimators for panel regression. Journal of Econometrics 168: 4853.
Heston, A., Summers, R., and Aten, B. 2011. Penn World Table Version 7.0. Philadelphia, PA: Center for International Comparisons of Production, Income, and Prices at the University of Pennsylvania.
Honda, Y. 1985. Testing the error components model with non-normal disturbances. Review of Economic Studies 52: 681–90.
Lake, D. A. 2007. Escape from the state of nature: Authority and hierarchy in world politics. International Security 32: 4779.
Levine, R. 2005. Finance and growth: Theory and evidence. In Handbook of Economic Growth, eds. Aghion, P. and Durlauf, S., 865934. Amsterdam: North-Holland.
Li, Q., and Schaub, D. 2004. Economic globalization and transnational terrorism: A pooled time-series analysis. Journal of Conflict Resolution 48: 230–58.
Mickolus, E. F., Sandler, T., Murdock, J. M., and Flemming, P. 2011. International terrorism: Attributes of terrorist events, 1968–2010 (ITERATE). Dunn Loring, VA: Vinyard Software.
National Consortium for the Study of Terrorism and Responses to Terrorism (START). 2011. Global terrorism database, University of Maryland. http://www.start.umd.edu/gtd (accessed April 9, 2012).
Nickell, S. J. 1981. Biases in dynamic model with fixed effects. Econometrica 49: 1417–26.
Pesaran, H. 2006. Estimation and inference in large heterogeneous panels with a multi-factor error structure. Econometrica 74: 9671012.
Phillips, P. C. B., and Sul, D. 2007. Bias in dynamic panel estimation with fixed effects, incidental trends and cross section dependence. Journal of Econometrics 137: 162–88.
Tavares, J. 2004. The open society assesses its enemies: Shocks, disasters and terrorist attacks. Journal of Monetary Economics 51: 1039–70.
Whitten, G. D., and Williams, L. K. 2001. Buttery guns and welfare hawks: The politics of defense spending in advanced industrial democracies. American Journal of Political Science 55: 117–34.
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Political Analysis
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