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

Published online by Cambridge University Press:  12 February 2015

JOHAN KOSKINEN ,
ALBERTO CAIMO  and
ALESSANDRO LOMI
JOHAN KOSKINEN
Affiliation:
Social Statistics Discipline Area, University of Manchester, Manchester M13 9PL, UK (e-mail: johan.koskinen@manchester.ac.uk)
ALBERTO CAIMO
Affiliation:
Faculty of Economics, University of Lugano, Switzerland
ALESSANDRO LOMI
Affiliation:
Faculty of Economics, University of Lugano, Switzerland
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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.

Keywords

dynamic networkslongitudinal exponential random graph modelforeign direct investment networkgravity modelspatial processesexchange samplerworld trade network topologyBayesian data augmentationcontinuous-time Markov chains
Type
Research Article
Information
Network Science , Volume 3 , Issue 1: Networks in Space and in Time: Methods and Applications , March 2015 , pp. 58 - 77
Copyright
Copyright © Cambridge University Press 2015 

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References

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=2160377Google Scholar
Anderson, J. E. (1979). A theoretical foundation for the gravity equation. American Economic Review, 69, 106–116.Google Scholar
Anderson, J. E. (2011). The gravity model. Annual Review of Economics, 3, 133–160.CrossRefGoogle Scholar
Anderson, J. E., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. The American Economic Review, 93, 170–192.Google Scholar
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, 269–342.Google Scholar
Bergstrand, J. H. (1985). The gravity equation in international trade: Come microeconomic foundations and empirical evidence. The Review of Economics and Statistics, 67, 474–481.Google Scholar
Bevan, A. A., & Estrin, S. (2004). The determinants of foreign direct investment into European transition economies. Journal of Comparative Economics, 32, 775–787.CrossRefGoogle Scholar
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, 1303–1325.CrossRefGoogle Scholar
Bortot, P., Coles, S. G., & Sisson, S. A. (2007). Inference for stereological extremes. Journal of the American Statistical Association, 102, 84–92.Google Scholar
Brakman, S., & van Bergeijk, P. (2010). The gravity model in international trade: Advances and applications. Cambridge: Cambridge University Press.Google Scholar
Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33, 41–55.Google Scholar
Caimo, A., & Friel, N. (2014). Bergm: Bayesian exponential random graphs in R. Journal of Statistical Software 61 (2).Google Scholar
Chakrabarti, A. (2003). A theory of the spatial distribution of foreign direct investment. International Review of Economics & Finance, 12, 149–169.Google Scholar
De Benedictis, L., & Tajoli, L. (2011). The world trade network. The World Economy, 34, 1417–1454.CrossRefGoogle Scholar
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, 6–17.Google Scholar
Dueñas, M., & Fagiolo, G. (2013). Modeling the international-trade network: A gravity approach. Journal of Economic Interaction and Cooridination, 8, 155–178.CrossRefGoogle Scholar
Fagiolo, G., Schiavo, S., & Reyes, J. (2009). World-trade web: Topological properties, dynamics, and evolution. Physical Review E, 79, 036115.Google Scholar
Feenstra, R. C. (2002). Border effects and the gravity equation: Consistent methods for estimation. Scottish Journal of Political Economy, 49, 491–506.Google Scholar
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832–842.CrossRefGoogle Scholar
Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711–732.Google Scholar
Head, K., & Mayer, T. (2014). Gravity equations: Workhorse, toolkit, and cookbook. In Gopinath, Helpman, & Rogoff (Eds.), Handbook of international economics, volume 4. Amsterdam: Elsevier.Google Scholar
Holland, P. W., & Leinhardt, S. (1977). A dynamic model for social networks. Journal of Mathematical Sociology, 5, 5–20.Google Scholar
Holland, P. W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs (with discussion). Journal of the American Statistical Association, 76, 33–65.CrossRefGoogle Scholar
Hunter, D. R., & Handcock, M. S. (2006). Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15, 565–583.Google Scholar
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.Google Scholar
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, 523–548.Google Scholar
Koskinen, J. H., & Snijders, T. A. B. (2007). Bayesian inference for dynamic social network data. Journal of Statistical Planning and Inference, 137, 3930–3938.Google Scholar
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, 147–176.Google Scholar
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, 324–328.Google Scholar
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.Google Scholar
Pattison, P., & Robins, G. L. (2002). Neighbourhood-based models for social networks. Sociological Methodology, 32, 301–337.Google Scholar
Redding, S. J. (2011). Theories of heterogeneous firms and trade. Annual Review of Economics, 3, 77–105.Google Scholar
Robins, G. L., & Pattison, P. E. (2001). Random graph models for temporal processes in social networks. Journal of Mathematical Sociology, 25, 5–41.Google Scholar
Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. In Sobel, M. E., & Becker, M. P. (Eds.), Sociological Methodology (pp. 361–395). London: Blackwell.Google Scholar
Snijders, T. A. B. (2002). Markov chain Monte Carlo estimation of exponential random graph models. Journal of Social Structure, 3 (2).Google Scholar
Snijders, T. A. B. (2006). Statistical methods for network dynamics. In Luchini, S. R. (Ed.), XLIII Scientific Meeting, Italian Statistical Society (pp. 281–296). Padova: CLEUP.Google Scholar
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. 130–140). New York: Cambridge University Press.Google Scholar
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.htmGoogle Scholar
Snijders, T. A. B., Koskinen, J. H., & Schweinberger, M. (2010). Maximum likelihood estimation for social network dynamics. Annals of Applied Statistics, 4, 567–588.CrossRefGoogle ScholarPubMed
Snijders, T. A. B., Pattison, P., Robins, G., & Handcock, M. (2006) New specifications for exponential random graph models. Sociological Methodology, 36, 99–153.CrossRefGoogle Scholar
Squartini, T., Fagiolo, G., & Garlaschelli, D. (2011a). Randomizing world trade. I. A binary network analysis. Physical Review E, 84, 046117.Google Scholar
Squartini, T., Fagiolo, G., & Garlaschelli, D. (2011b). Randomizing world trade. II. A weighted network analysis. Physical Review E, 84, 046118.CrossRefGoogle Scholar
Wasserman, S. (1980). Analyzing social networks as stochastic processes. Journal of the American Statistical Association, 75, 280–294.CrossRefGoogle Scholar
Wasserman, S., & Pattison, P. E. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61, 401–425.Google Scholar