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Dynamic Panel Analysis under Cross-Sectional Dependence

Published online by Cambridge University Press:  04 January 2017

Khusrav Gaibulloev
Department of Economics, American University of Sharjah, Sharjah, UAE. e-mail:
Todd Sandler*
School of Economic, Political, and Policy Sciences, University of Texas at Dallas, GR 31, 800 W. Campbell Road, Richardson, TX 75080, USA
Donggyu Sul
School of Economic, Political, and Policy Sciences, University of Texas at Dallas, GR 31, 800 W. Campbell Road, Richardson, TX 75080, USA. e-mail:
e-mail: (corresponding author)
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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.

Research Article
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This is an Open-Access article, distributed under the terms of the Creative Commons Attribution license (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © The Author 2014. Published by Oxford University Press on behalf of the Society for Political Methodology


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 Supplementary materials for this article are available on the Political Analysis Web site.


Alvarez, J., and Arellano, M. 2003. The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71: 1121–59.CrossRefGoogle Scholar
Arellano, M. 1993. On the testing of correlated effects with panel data. Journal of Econometrics 59: 8797.CrossRefGoogle Scholar
Bafumi, J., and Gelman, A. 2006. Fitting multilevel models when predictors and group effects correlate. Working paper,∼gelman/research/unpublished/Bafumi_Gelman_Midwest06.pdf (accessed May 26, 2013).Google Scholar
Bai, J. 2009. Panel data models with interactive fixed effects. Econometrica 77: 1229–79.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Blomberg, S. B., Hess, G. D., and Orphanides, A. 2004. The macroeconomic consequences of terrorism. Journal of Monetary Economics 51: 1007–32.CrossRefGoogle Scholar
Brinks, D., and Coppedge, M. 2006. Diffusion is no illusion: Neighbor emulation in the third wave of democracy. Comparative Political Studies 39: 463–89.CrossRefGoogle Scholar
Chambers, D., and Guo, J.-T. 2009. Natural resources and economic growth: Some theory and evidence. Annals of Economics and Finance 10: 367–89.Google Scholar
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.CrossRefGoogle Scholar
Cieślik, A., and Tarsalewska, M. 2011. External openness and economic growth in developing countries. Review of Development Economics 15: 729–44.CrossRefGoogle Scholar
Enders, W., and Sandler, T. 2005. After 9/11: Is it all different now? Journal of Conflict Resolution 49: 259–77.CrossRefGoogle Scholar
Enders, W., Sandler, T., and Gaibulloev, K. 2011. Domestic versus transnational terrorism: Data, decomposition, and dynamic. Journal of Peace Research 48: 319–37.CrossRefGoogle Scholar
Engene, J. O. 2007. Five decades of terrorism in Europe: The TWEED dataset. Journal of Peace Research 44: 109–21.CrossRefGoogle Scholar
Gaibulloev, K., and Sandler, T. 2008. Growth consequences of terrorism in Western Europe. Kyklos 61: 411–24.CrossRefGoogle Scholar
Gaibulloev, K., and Sandler, T. 2009. The impact of terrorism and conflicts on growth in Asia. Economics & Politics 21: 359–83.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Gaibulloev, K., Sandler, T., and Sul, D. 2013. Common drivers of transnational terrorism: Principal component analysis. Economic Inquiry 51: 707–21.Google Scholar
Gaibulloev, K., Sandler, T., and Sul, D. 2014. Replication data for: Dynamic panel analysis under cross-sectional dependence. IQSS Dataverse Network [Distributor] VI [Version] (accessed November 13, 2013).Google Scholar
Garrett, G., and Mitchell, D. 2001. Globalization, government spending and taxation in the OECD. European Journal of Political Research 39: 145–77.CrossRefGoogle Scholar
Green, D. P., Kim, S. Y., and Yoon, D. H. 2001. Dirty pool. International Organization 55: 441–68.CrossRefGoogle Scholar
Greenaway-McGrevy, R., Han, C., and Sul, D. 2012. Asymptotic distribution of factor augmented estimators for panel regression. Journal of Econometrics 168: 4853.CrossRefGoogle Scholar
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.Google Scholar
Honda, Y. 1985. Testing the error components model with non-normal disturbances. Review of Economic Studies 52: 681–90.CrossRefGoogle Scholar
Lake, D. A. 2007. Escape from the state of nature: Authority and hierarchy in world politics. International Security 32: 4779.CrossRefGoogle Scholar
Levine, R. 2005. Finance and growth: Theory and evidence. In Handbook of Economic Growth, eds. Aghion, P. and Durlauf, S., 865934. Amsterdam: North-Holland.Google Scholar
Li, Q., and Schaub, D. 2004. Economic globalization and transnational terrorism: A pooled time-series analysis. Journal of Conflict Resolution 48: 230–58.CrossRefGoogle Scholar
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.Google Scholar
National Consortium for the Study of Terrorism and Responses to Terrorism (START). 2011. Global terrorism database, University of Maryland. (accessed April 9, 2012).Google Scholar
Nickell, S. J. 1981. Biases in dynamic model with fixed effects. Econometrica 49: 1417–26.CrossRefGoogle Scholar
Pesaran, H. 2006. Estimation and inference in large heterogeneous panels with a multi-factor error structure. Econometrica 74: 9671012.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Tavares, J. 2004. The open society assesses its enemies: Shocks, disasters and terrorist attacks. Journal of Monetary Economics 51: 1039–70.CrossRefGoogle Scholar
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.Google Scholar
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