Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
This course will provide an overview of econometric methods appropriate for the analysis of social and economic networks. Many social and economic activities are em- bedded in networks. Furthermore, datasets with natural graph theoretic (i.e., network) structure are increasingly available to researchers. We will review (i) how to describe, summarize and visually present network data and (ii) formal econometric models of network formation, including ones that admit heterogeneity and/or strategic behavior. Special emphasis will be placed on parametric and non-parametric methods appropri- ate for dyadic analysis – as arises in, for example, the analysis of international trade or migration flows.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
This course will provide an overview of econometric methods appropriate for the analysis of social and economic networks. Many social and economic activities are em- bedded in networks. Furthermore, datasets with natural graph theoretic (i.e., network) structure are increasingly available to researchers. We will review (i) how to describe, summarize and visually present network data and (ii) formal econometric models of network formation, including ones that admit heterogeneity and/or strategic behavior. Special emphasis will be placed on parametric and non-parametric methods appropri- ate for dyadic analysis – as arises in, for example, the analysis of international trade or migration flows.
Podmínky zakončení předmětu -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
Grading - in line with the Dean's decree 17/2018.
Final exam in the form of home assignment.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
Grading - in line with the Dean's decree 17/2018.
Final exam in the form of home assignment.
Literatura -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
Anderson, J. E. (2011). The gravity model. Annual Review in Economics, 3(1), 133 – 160. Apicella, C. L., Marlowe, F. W., Fowler, J. H., & Christakis, N. A. (2012). Social networks and cooperation in hunter-gatherers. Nature, 481(7382), 497 – 501.
Aronow, P. M., Samii, C., & Assenova, V. A. (2017). Cluster-robust variance estimation for dyadic data. Political Analysis, 23(4), 564 – 577.
Atalay, E., Hortaçsu, A., Roberts, J., & Syverson, C. (2011). Network structure of production. Proceedings of the National Academy of Sciences, 108(13), 5199 – 5202.
Blitzstein, J. & Diaconis, P. (2011). A sequential importance sampling algorithm for generatingrandom graphs with prescribed degrees. Internet Mathematics, 6(4), 489 – 522.
Chatterjee, S., Diaconis, P., & Sly, A. (2011). Random graphs with a given degree sequence. Annalsof Applied Probability, 21(4), 1400 – 1435.
Dzemski, A. (2018). An empirical model of dyadic link formation in a network with unobservedheterogeneity. Review of Economics and Statistics. University of Mannheim.
Fafchamps, M. & Gubert, F. (2007). The formation of risk sharing networks. Journal of Develop-ment Economics, 83(2), 326 – 350.
Goldenberg, A., Zheng, A., Fienberg, S. E., & Airoldi, E. M. (2009). A survey of statistical network models. Foundations and Trends in Machine Learning, 2(2), 129–333.
Graham, B. S. (2017). An econometric model of network formation with degree heterogeneity. Econometrica, 85(4), 1033 – 1063.
Graham, B. S., Niu, F., & Powell, J. L. (2019). Kernel density estimation for undirected dyadic data. Technical report, University of California - Berkeley.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360– 1380.
Guttag, J. V. (2013). Introduction to Computation and Programming Using Python. Cambridge, MA: The MIT Press.
Holland, P. W. & Leinhardt, S. (1976). Local structure in social networks. Sociological Methodology, 7, 1 – 45.
Jackson, M. (2006). The economics of social networks. In R. Blundell, W. Newey, & T. Pers- son (Eds.), Advances in Economics and Econometrics, Theory and Applications: Ninth World Congress of the Econometric Society. Cambridge: Cambridge University Press.
Jackson, M. O., Rodriguez-Barraquer, T., & Tan, X. (2012). Social capital and social quilts: network patterns of favor exchange. American Economic Review, 102(5), 1857–1897.
Jackson, M. O., Rogers, B. W., & Zenou, Y. (2017). The economic consequences of social-network structure. Journal of Economic Literature, 55(1), 49 – 95.
Jochmans, K. (2018). Semiparametric analysis of network formation. Journal of Business and Economic Statistics.
König, M. D., Liu, X., & Zenou, Y. (2018). R&d networks: theory, empirics and policy implications. Review of Economics and Statistics.
McDonald, J. W., Smith, P. W. F., & Forster, J. J. (2007). Markov chain monte carlo exact inference for social networks. Social Networks, 29(1), 127 – 136.
McKinney, W. (2017). Python for Data Analysis. Cambridge: O’Reilly.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: homophily in social networks. Annual Review of Sociology, 27(1), 415 – 444.
Menzel, K. (2017). Bootstrap with clustering in two or more dimensions. Technical Report 1703.03043v2, arXiv.
Milgram, S. (1967). The small-world problem. Psychology Today, 1(1), 61 – 67.
Mitzenmacher, M. (2004). A brief history of generative models for power law and lognormal distributions. Internet Mathematics, 1(2), 226 – 251.
Miyauchi, Y. (2016). Structural estimation of a pairwise stable network with nonnegative exter- nality. Journal of Econometrics, 195(2), 224 – 235.
Mizuno, T., Souma, W., & Watanabe, T. (2014). The structure and evolution of buyer-supplier networks. Plos One, 9(7), e100712.
Newman, M. E. J. (2010). Networks: An Introduction. Oxford: Oxford University Press.
Pelican, A. & Graham, B. S. (2019). Testing for strategic interaction in social and economic network formation. Technical report, University of California - Berkeley.
Santos Silva, J. & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88(4), 641 – 658.
VanderPlas, J. (2017). Python Data Science Handbook. Boston: O’Reilly.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
Anderson, J. E. (2011). The gravity model. Annual Review in Economics, 3(1), 133 – 160. Apicella, C. L., Marlowe, F. W., Fowler, J. H., & Christakis, N. A. (2012). Social networks and cooperation in hunter-gatherers. Nature, 481(7382), 497 – 501.
Aronow, P. M., Samii, C., & Assenova, V. A. (2017). Cluster-robust variance estimation for dyadic data. Political Analysis, 23(4), 564 – 577.
Atalay, E., Hortaçsu, A., Roberts, J., & Syverson, C. (2011). Network structure of production. Proceedings of the National Academy of Sciences, 108(13), 5199 – 5202.
Blitzstein, J. & Diaconis, P. (2011). A sequential importance sampling algorithm for generatingrandom graphs with prescribed degrees. Internet Mathematics, 6(4), 489 – 522.
Chatterjee, S., Diaconis, P., & Sly, A. (2011). Random graphs with a given degree sequence. Annalsof Applied Probability, 21(4), 1400 – 1435.
Dzemski, A. (2018). An empirical model of dyadic link formation in a network with unobservedheterogeneity. Review of Economics and Statistics. University of Mannheim.
Fafchamps, M. & Gubert, F. (2007). The formation of risk sharing networks. Journal of Develop-ment Economics, 83(2), 326 – 350.
Goldenberg, A., Zheng, A., Fienberg, S. E., & Airoldi, E. M. (2009). A survey of statistical network models. Foundations and Trends in Machine Learning, 2(2), 129–333.
Graham, B. S. (2017). An econometric model of network formation with degree heterogeneity. Econometrica, 85(4), 1033 – 1063.
Graham, B. S., Niu, F., & Powell, J. L. (2019). Kernel density estimation for undirected dyadic data. Technical report, University of California - Berkeley.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360– 1380.
Guttag, J. V. (2013). Introduction to Computation and Programming Using Python. Cambridge, MA: The MIT Press.
Holland, P. W. & Leinhardt, S. (1976). Local structure in social networks. Sociological Methodology, 7, 1 – 45.
Jackson, M. (2006). The economics of social networks. In R. Blundell, W. Newey, & T. Pers- son (Eds.), Advances in Economics and Econometrics, Theory and Applications: Ninth World Congress of the Econometric Society. Cambridge: Cambridge University Press.
Jackson, M. O., Rodriguez-Barraquer, T., & Tan, X. (2012). Social capital and social quilts: network patterns of favor exchange. American Economic Review, 102(5), 1857–1897.
Jackson, M. O., Rogers, B. W., & Zenou, Y. (2017). The economic consequences of social-network structure. Journal of Economic Literature, 55(1), 49 – 95.
Jochmans, K. (2018). Semiparametric analysis of network formation. Journal of Business and Economic Statistics.
König, M. D., Liu, X., & Zenou, Y. (2018). R&d networks: theory, empirics and policy implications. Review of Economics and Statistics.
McDonald, J. W., Smith, P. W. F., & Forster, J. J. (2007). Markov chain monte carlo exact inference for social networks. Social Networks, 29(1), 127 – 136.
McKinney, W. (2017). Python for Data Analysis. Cambridge: O’Reilly.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: homophily in social networks. Annual Review of Sociology, 27(1), 415 – 444.
Menzel, K. (2017). Bootstrap with clustering in two or more dimensions. Technical Report 1703.03043v2, arXiv.
Milgram, S. (1967). The small-world problem. Psychology Today, 1(1), 61 – 67.
Mitzenmacher, M. (2004). A brief history of generative models for power law and lognormal distributions. Internet Mathematics, 1(2), 226 – 251.
Miyauchi, Y. (2016). Structural estimation of a pairwise stable network with nonnegative exter- nality. Journal of Econometrics, 195(2), 224 – 235.
Mizuno, T., Souma, W., & Watanabe, T. (2014). The structure and evolution of buyer-supplier networks. Plos One, 9(7), e100712.
Newman, M. E. J. (2010). Networks: An Introduction. Oxford: Oxford University Press.
Pelican, A. & Graham, B. S. (2019). Testing for strategic interaction in social and economic network formation. Technical report, University of California - Berkeley.
Santos Silva, J. & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88(4), 641 – 658.
VanderPlas, J. (2017). Python Data Science Handbook. Boston: O’Reilly.
Požadavky ke zkoušce -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
Grading - in line with the Dean's decree 17/2018.
Final exam in the form of home assignment.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
Grading - in line with the Dean's decree 17/2018.
Final exam in the form of home assignment.
Sylabus -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
Topic 1
Describing Networks Examples of networks Small worlds Degree distributions Homophily Triads
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
The equivalent of a first year Ph.D. level sequence in econometrics. Specifically an understanding of probability and statistical inference at the level of Casella and Berger (1990, Statistical Inference), linear regression analysis at the level of Goldberger (1991, A Course in Econometrics) and some exposure to non-linear models (e.g., maximum likelihood, M-estimation, GMM). I will also assume a basic knowledge of applied linear/matrix algebra.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
The equivalent of a first year Ph.D. level sequence in econometrics. Specifically an understanding of probability and statistical inference at the level of Casella and Berger (1990, Statistical Inference), linear regression analysis at the level of Goldberger (1991, A Course in Econometrics) and some exposure to non-linear models (e.g., maximum likelihood, M-estimation, GMM). I will also assume a basic knowledge of applied linear/matrix algebra.
Požadavky k zápisu -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
The equivalent of a first year Ph.D. level sequence in econometrics. Specifically an understanding of probability and statistical inference at the level of Casella and Berger (1990, Statistical Inference), linear regression analysis at the level of Goldberger (1991, A Course in Econometrics) and some exposure to non-linear models (e.g., maximum likelihood, M-estimation, GMM). I will also assume a basic knowledge of applied linear/matrix algebra.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (28.10.2019)
The equivalent of a first year Ph.D. level sequence in econometrics. Specifically an understanding of probability and statistical inference at the level of Casella and Berger (1990, Statistical Inference), linear regression analysis at the level of Goldberger (1991, A Course in Econometrics) and some exposure to non-linear models (e.g., maximum likelihood, M-estimation, GMM). I will also assume a basic knowledge of applied linear/matrix algebra.
