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Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to Foreign Direct Investments

  • JOHAN KOSKINEN (a1), ALBERTO CAIMO (a2) and ALESSANDRO LOMI (a2)

Abstract

In dynamic networks, the presence of ties are subject both to endogenous network dependencies and spatial dependencies. Current statistical models for change over time are typically defined relative to some initial condition, thus skirting the issue of where the first network came from. Additionally, while these longitudinal network models may explain the dynamics of change in the network over time, they do not explain the change in those dynamics. We propose an extension to the longitudinal exponential random graph model that allows for simultaneous inference of the changes over time and the initial conditions, as well as relaxing assumptions of time-homogeneity. Estimation draws on recent Bayesian approaches for cross-sectional exponential random graph models and Bayesian hierarchical models. This is developed in the context of foreign direct investment relations in the global electricity industry in 1995–2003. International investment relations are known to be affected by factors related to: (i) the initial conditions determined by the geographical locations; (ii) time-dependent fluctuations in the global intensity of investment flows; and (iii) endogenous network dependencies. We rely on the well-known gravity model used in research on international trade to represent how spatial embedding and endogenous network dependencies jointly shape the dynamics of investment relations.

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Abbate, A., De Benedictis, L., Fagiolo, G., & Tajoli, L. (2012). The international trade network in space and time. LEM Working Paper Series, Institute of Economics Scuola Superiore Sant'Anna. Available at SSRN: http://ssrn.com/abstract=2160377
Anderson, J. E. (1979). A theoretical foundation for the gravity equation. American Economic Review, 69, 106116.
Anderson, J. E. (2011). The gravity model. Annual Review of Economics, 3, 133160.
Anderson, J. E., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. The American Economic Review, 93, 170192.
Andrieu, C., Doucet, A., & Holenstein, R. (2010). Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72.3, 269342.
Bergstrand, J. H. (1985). The gravity equation in international trade: Come microeconomic foundations and empirical evidence. The Review of Economics and Statistics, 67, 474481.
Bevan, A. A., & Estrin, S. (2004). The determinants of foreign direct investment into European transition economies. Journal of Comparative Economics, 32, 775787.
Blonigen, B. A., Davies, R. B., Waddell, G. R., & Naughton, H. T. (2007). FDI in space: Spatial autoregressive relationships in foreign direct investment. European Economic Review, 51, 13031325.
Bortot, P., Coles, S. G., & Sisson, S. A. (2007). Inference for stereological extremes. Journal of the American Statistical Association, 102, 8492.
Brakman, S., & van Bergeijk, P. (2010). The gravity model in international trade: Advances and applications. Cambridge: Cambridge University Press.
Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33, 4155.
Caimo, A., & Friel, N. (2014). Bergm: Bayesian exponential random graphs in R. Journal of Statistical Software 61 (2).
Chakrabarti, A. (2003). A theory of the spatial distribution of foreign direct investment. International Review of Economics & Finance, 12, 149169.
De Benedictis, L., & Tajoli, L. (2011). The world trade network. The World Economy, 34, 14171454.
Daraganova, G., Pattison, P., Koskinen, J., Mitchell, B., Bill, A., Watts, M., & Baum, S. (2012). Networks and geography: Modelling community network structures as the outcome of both spatial and network processes. Social Networks, 34, 617.
Dueñas, M., & Fagiolo, G. (2013). Modeling the international-trade network: A gravity approach. Journal of Economic Interaction and Cooridination, 8, 155178.
Fagiolo, G., Schiavo, S., & Reyes, J. (2009). World-trade web: Topological properties, dynamics, and evolution. Physical Review E, 79, 036115.
Feenstra, R. C. (2002). Border effects and the gravity equation: Consistent methods for estimation. Scottish Journal of Political Economy, 49, 491506.
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832842.
Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711732.
Head, K., & Mayer, T. (2014). Gravity equations: Workhorse, toolkit, and cookbook. In Gopinath, Helpman, & Rogoff (Eds.), Handbook of international economics, volume 4. Amsterdam: Elsevier.
Holland, P. W., & Leinhardt, S. (1977). A dynamic model for social networks. Journal of Mathematical Sociology, 5, 520.
Holland, P. W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs (with discussion). Journal of the American Statistical Association, 76, 3365.
Hunter, D. R., & Handcock, M. S. (2006). Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15, 565583.
Igarashi, T. (2013). Longitudinal changes in face-to-face and text message-mediated friendship networks. In Lusher, D., Koskinen, J. H., & Robins, G. F. (Eds.), Exponential random graph models for social networks: Theory, methods and applications (pp. 248–259). New York: Cambridge University Press.
Koskinen, J. H., & Lomi, A. (2013). The local structure of globalization: The network dynamics of foreign direct investments in the international electricity industry. Journal of Statistical Physics, 151, 523548.
Koskinen, J. H., & Snijders, T. A. B. (2007). Bayesian inference for dynamic social network data. Journal of Statistical Planning and Inference, 137, 39303938.
Lospinoso, J. A., Schweinberger, M., Snijders, T. A. B., & Ripley, R. M. (2011). Assessing and accounting for time heterogeneity in stochastic actor oriented models. Advances in Data Analysis and Computation, 5, 147176.
Marjoram, P., Molitor, J., Plagnol, V., & Tavare, S. (2003). Markov chain Monte Carlo without likelihoods Proceedings of the National Academy of Sciences of the United States, 100, 324328.
Murray, I., Ghahramani, Z. & MacKay, D. (2006). MCMC for doubly-intractable distributions. In Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-06). AUAI Press, Arlington, Virginia.
Pattison, P., & Robins, G. L. (2002). Neighbourhood-based models for social networks. Sociological Methodology, 32, 301337.
Redding, S. J. (2011). Theories of heterogeneous firms and trade. Annual Review of Economics, 3, 77105.
Robins, G. L., & Pattison, P. E. (2001). Random graph models for temporal processes in social networks. Journal of Mathematical Sociology, 25, 541.
Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. In Sobel, M. E., & Becker, M. P. (Eds.), Sociological Methodology (pp. 361395). London: Blackwell.
Snijders, T. A. B. (2002). Markov chain Monte Carlo estimation of exponential random graph models. Journal of Social Structure, 3 (2).
Snijders, T. A. B. (2006). Statistical methods for network dynamics. In Luchini, S. R. (Ed.), XLIII Scientific Meeting, Italian Statistical Society (pp. 281296). Padova: CLEUP.
Snijders, T. A. B., & Koskinen, J. (2013) Longitudinal models. In Lusher, D., Koskinen, J., & Robins, G. (Eds.), Exponential random graph models for social networks: Theory, methods and applications (pp. 130140). New York: Cambridge University Press.
Snijders, T. A. B., & Koskinen, J. (2012). Multilevel longitudinal analysis of social networks. Paper presented at the 8th UKSNA Conference, Bristol, June 28–30. http://www.stats.ox.ac.uk/~snijders/siena/siena_articles.htm
Snijders, T. A. B., Koskinen, J. H., & Schweinberger, M. (2010). Maximum likelihood estimation for social network dynamics. Annals of Applied Statistics, 4, 567588.
Snijders, T. A. B., Pattison, P., Robins, G., & Handcock, M. (2006) New specifications for exponential random graph models. Sociological Methodology, 36, 99153.
Squartini, T., Fagiolo, G., & Garlaschelli, D. (2011a). Randomizing world trade. I. A binary network analysis. Physical Review E, 84, 046117.
Squartini, T., Fagiolo, G., & Garlaschelli, D. (2011b). Randomizing world trade. II. A weighted network analysis. Physical Review E, 84, 046118.
Wasserman, S. (1980). Analyzing social networks as stochastic processes. Journal of the American Statistical Association, 75, 280294.
Wasserman, S., & Pattison, P. E. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61, 401425.
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Network Science
  • ISSN: 2050-1242
  • EISSN: 2050-1250
  • URL: /core/journals/network-science
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