STRUCTURAL ECONOMETRIC MODELLING
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES:
-This course will provide an overview of econometric methodologies used to study and develop structural models.
-Students will gain knowledge on identifying and estimating model parameters based on theoretical economic models.
-The course will exploit the link between the literature of structural econometrics and causal inference in statistics.
-These methodologies will then be applied, for example, in the evaluation of well-known policy programs.
KNOWLEDGE AND UNDERSTANDING:
Learn the main statistical and econometric approaches to identify and estimate structural econometric models.
APPLYING KNOWLEDGE AND UNDERSTANDING:
-Apply knowledge and understanding of the estimation techniques by utilizing microdata in policy evaluation scenarios.
-Applications will be based on widely recognized welfare programs.
-Develop skills in using the following software: R.
MAKING JUDGEMENTS:
-Develop microdata analysis skills useful for preparing research projects.
-Understand the difference between 'structural econometric' and 'reduced form' approaches.
COMMUNICATION SKILLS:
-Learn to present facts, analyze data, and address economic problems rigorously for both specialist and non-specialist audiences.
LEARNING SKILLS:
-Gain the ability to estimate parameters of structural models and interpret economic issues within the context of existing economic theories.
-Apply these research techniques to independently develop research projects and ideas.
-This course will provide an overview of econometric methodologies used to study and develop structural models.
-Students will gain knowledge on identifying and estimating model parameters based on theoretical economic models.
-The course will exploit the link between the literature of structural econometrics and causal inference in statistics.
-These methodologies will then be applied, for example, in the evaluation of well-known policy programs.
KNOWLEDGE AND UNDERSTANDING:
Learn the main statistical and econometric approaches to identify and estimate structural econometric models.
APPLYING KNOWLEDGE AND UNDERSTANDING:
-Apply knowledge and understanding of the estimation techniques by utilizing microdata in policy evaluation scenarios.
-Applications will be based on widely recognized welfare programs.
-Develop skills in using the following software: R.
MAKING JUDGEMENTS:
-Develop microdata analysis skills useful for preparing research projects.
-Understand the difference between 'structural econometric' and 'reduced form' approaches.
COMMUNICATION SKILLS:
-Learn to present facts, analyze data, and address economic problems rigorously for both specialist and non-specialist audiences.
LEARNING SKILLS:
-Gain the ability to estimate parameters of structural models and interpret economic issues within the context of existing economic theories.
-Apply these research techniques to independently develop research projects and ideas.
TIZIANO ARDUINI
Prerequisites
microeconometrics and statistics
Program
"Man is by nature a social animal... He who lives without society is either a beast or God" (Aristotle - Politics, Book I, Part II).
In economics, the importance of social interactions outside the market is now widely recognized. Individuals share information, learn from each other, and influence one another in various contexts.
The course introduces the game-theoretical foundations of social interaction models and focuses on identifying and structurally estimating model parameters. Social interaction models are a specific case of simultaneous equations models, i.e., statistical models in which dependent variables are jointly determined by other dependent variables along with independent ones. Many economic models are simultaneous in nature as a result of the underlying equilibrium mechanism. A prime example is the estimation of utility parameters within the equilibrium equation system in the economy under social interactions.
Module 1: Description of Networks - Centrality Measures
Bonacich, P. (1987), Power and centrality: A family of measures, American Journal of Sociology, 92(5): 1170-1182.
Katz L., "A new index derived from sociometric data analysis," Psychometrika, 18: 39-43.
Module 2: Social Interaction Models - Microfoundations and Transition from Model to Data
Bramoullé, Y., Djebbari, H., and Fortin, B., 2020. Peer effects in networks: A survey. Annual Review of Economics, 12, pp.603-629.
De Paula, A., January 2017. Econometrics of network models. In Advances in Economics and Econometrics: Theory and Applications: Eleventh World Congress (Vol. 1, pp. 268-323). Cambridge: Cambridge University Press.
Blume, L.E., Brock, W.A., Durlauf, S.N., and Jayaraman, R., 2015. Linear social interaction models. Journal of Political Economy, 123(2), pp.444-496.
Calvó-Arkmengol, A., Patacchini, E., and Zenou, Y., 2009. Peer effects and social networks in education. The Review of Economic Studies, 76(4), pp.1239-1267.
Module 3: Identification and Estimation - Statistical Model, Reduced Form, Identification, Estimation, and Specification Tests
Manski, C.F., 1993. Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60(3), pp.531-542.
Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors, and fixed effects. Journal of Econometrics, 140(2), pp.333-374.
Bramoullé, Y., Djebbari, H., and Fortin, B., 2009. Identification of peer effects through social networks. Journal of Econometrics, 150(1), pp.41-55.
Lee, L.F., Liu, X., and Lin, X., 2010. "Specification and estimation of social interaction models with network structures." The Econometrics Journal, 13(2), pp.145-176.
Liu, X., and Lee, L.F., 2010. GMM estimation of social interaction models with centrality. Journal of Econometrics, 159(1), pp.99-115.
Module 4: Model Extensions - Endogeneity, Quasi-Random Variations, Heterogeneity, and Treatment Effects
Angrist, J.D., 2014. The perils of peer effects. Labour Economics, 30, pp.98-108.
Arduini, T., Patacchini, E., and Rainone, E., 2020. Identification and estimation of network models with heterogeneous interactions. In The Econometrics of Networks. Emerald Publishing Limited.
Arduini, T., Patacchini, E., and Rainone, E., 2020. Treatment effects with heterogeneous externalities. Journal of Business & Economic Statistics, 38(4), pp.826-838.
Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. The effect of classmate characteristics on post-secondary outcomes: Evidence from the Add Health. American Economic Journal: Economic Policy 3.1 (2011): 25-53.
Johnsson, I., and Moon, H.R., 2021. Estimation of peer effects in endogenous social networks: Control function approach. Review of Economics and Statistics, 103(2), pp.328-345.
For a complete list of references, please refer to the slides.
In economics, the importance of social interactions outside the market is now widely recognized. Individuals share information, learn from each other, and influence one another in various contexts.
The course introduces the game-theoretical foundations of social interaction models and focuses on identifying and structurally estimating model parameters. Social interaction models are a specific case of simultaneous equations models, i.e., statistical models in which dependent variables are jointly determined by other dependent variables along with independent ones. Many economic models are simultaneous in nature as a result of the underlying equilibrium mechanism. A prime example is the estimation of utility parameters within the equilibrium equation system in the economy under social interactions.
Module 1: Description of Networks - Centrality Measures
Bonacich, P. (1987), Power and centrality: A family of measures, American Journal of Sociology, 92(5): 1170-1182.
Katz L., "A new index derived from sociometric data analysis," Psychometrika, 18: 39-43.
Module 2: Social Interaction Models - Microfoundations and Transition from Model to Data
Bramoullé, Y., Djebbari, H., and Fortin, B., 2020. Peer effects in networks: A survey. Annual Review of Economics, 12, pp.603-629.
De Paula, A., January 2017. Econometrics of network models. In Advances in Economics and Econometrics: Theory and Applications: Eleventh World Congress (Vol. 1, pp. 268-323). Cambridge: Cambridge University Press.
Blume, L.E., Brock, W.A., Durlauf, S.N., and Jayaraman, R., 2015. Linear social interaction models. Journal of Political Economy, 123(2), pp.444-496.
Calvó-Arkmengol, A., Patacchini, E., and Zenou, Y., 2009. Peer effects and social networks in education. The Review of Economic Studies, 76(4), pp.1239-1267.
Module 3: Identification and Estimation - Statistical Model, Reduced Form, Identification, Estimation, and Specification Tests
Manski, C.F., 1993. Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60(3), pp.531-542.
Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors, and fixed effects. Journal of Econometrics, 140(2), pp.333-374.
Bramoullé, Y., Djebbari, H., and Fortin, B., 2009. Identification of peer effects through social networks. Journal of Econometrics, 150(1), pp.41-55.
Lee, L.F., Liu, X., and Lin, X., 2010. "Specification and estimation of social interaction models with network structures." The Econometrics Journal, 13(2), pp.145-176.
Liu, X., and Lee, L.F., 2010. GMM estimation of social interaction models with centrality. Journal of Econometrics, 159(1), pp.99-115.
Module 4: Model Extensions - Endogeneity, Quasi-Random Variations, Heterogeneity, and Treatment Effects
Angrist, J.D., 2014. The perils of peer effects. Labour Economics, 30, pp.98-108.
Arduini, T., Patacchini, E., and Rainone, E., 2020. Identification and estimation of network models with heterogeneous interactions. In The Econometrics of Networks. Emerald Publishing Limited.
Arduini, T., Patacchini, E., and Rainone, E., 2020. Treatment effects with heterogeneous externalities. Journal of Business & Economic Statistics, 38(4), pp.826-838.
Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. The effect of classmate characteristics on post-secondary outcomes: Evidence from the Add Health. American Economic Journal: Economic Policy 3.1 (2011): 25-53.
Johnsson, I., and Moon, H.R., 2021. Estimation of peer effects in endogenous social networks: Control function approach. Review of Economics and Statistics, 103(2), pp.328-345.
For a complete list of references, please refer to the slides.
Books
Material related to lectures and the reading list will be provided on the course TEAMs page and the relevant Stata/R code will be shared with the students.
Bibliography
Bonacich, P. (1987), Power and centrality: A family of measures, American Journal of Sociology, 92(5): 1170-1182.
Katz L., `A new index derived from sociometric data analysis, Psychometrika, 18: 39-43.
Bramoullé, Y., Djebbari, H. and Fortin, B., 2020. Peer effects in networks: A survey. Annual Review of Economics, 12, pp.603-629.
De Paula, A., 2017, January. Econometrics of network models. In Advances in Economics and Econometrics: Theory and Applications: Eleventh World Congress(Vol. 1, pp. 268-323). Cambridge: Cambridge University Press.
Blume, L.E., Brock, W.A., Durlauf, S.N. and Jayaraman, R., 2015. Linear social interactions models. Journal of Political Economy, 123(2), pp.444-496.
Calvó-Arkmengol, A., Patacchini, E. and Zenou, Y., 2009. Peer effects and social networks in education. The review of economic studies, 76(4), pp.1239-1267.
Manski, C.F., 1993. Identification of endogenous social effects: The reflection problem. The review of economic studies, 60(3), pp.531-542.
Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics, 140(2), pp.333-374.
Bramoullé, Y., Djebbari, H. and Fortin, B., 2009. Identification of peer effects through social networks. Journal of econometrics, 150(1), pp.41-55.
Lee, L.F., Liu, X. and Lin, X., 2010. `` Specification and estimation of social interaction models with network structures". The Econometrics Journal, 13(2), pp.145-176.
Liu, X. and Lee, L.F., 2010. GMM estimation of social interaction models with centrality. Journal of Econometrics, 159(1), pp.99-115.
Angrist, J.D., 2014. The perils of peer effects. Labour Economics, 30, pp.98-108.
Arduini, T., Patacchini, E. and Rainone, E., 2020. Identification and estimation of network models with heterogeneous interactions. In The Econometrics of Networks. Emerald Publishing Limited.
Arduini, T., Patacchini, E. and Rainone, E., 2020. Treatment effects with heterogeneous externalities. Journal of Business & Economic Statistics, 38(4), pp.826-838.
Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. The effect of classmate characteristics on post-secondary outcomes: Evidence from the Add Health. American Economic Journal: Economic Policy 3.1 (2011): 25-53.
Johnsson, I. and Moon, H.R., 2021. Estimation of peer effects in endogenous social networks: Control function approach. Review of Economics and Statistics, 103(2), pp.328-345.
Katz L., `A new index derived from sociometric data analysis, Psychometrika, 18: 39-43.
Bramoullé, Y., Djebbari, H. and Fortin, B., 2020. Peer effects in networks: A survey. Annual Review of Economics, 12, pp.603-629.
De Paula, A., 2017, January. Econometrics of network models. In Advances in Economics and Econometrics: Theory and Applications: Eleventh World Congress(Vol. 1, pp. 268-323). Cambridge: Cambridge University Press.
Blume, L.E., Brock, W.A., Durlauf, S.N. and Jayaraman, R., 2015. Linear social interactions models. Journal of Political Economy, 123(2), pp.444-496.
Calvó-Arkmengol, A., Patacchini, E. and Zenou, Y., 2009. Peer effects and social networks in education. The review of economic studies, 76(4), pp.1239-1267.
Manski, C.F., 1993. Identification of endogenous social effects: The reflection problem. The review of economic studies, 60(3), pp.531-542.
Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics, 140(2), pp.333-374.
Bramoullé, Y., Djebbari, H. and Fortin, B., 2009. Identification of peer effects through social networks. Journal of econometrics, 150(1), pp.41-55.
Lee, L.F., Liu, X. and Lin, X., 2010. `` Specification and estimation of social interaction models with network structures". The Econometrics Journal, 13(2), pp.145-176.
Liu, X. and Lee, L.F., 2010. GMM estimation of social interaction models with centrality. Journal of Econometrics, 159(1), pp.99-115.
Angrist, J.D., 2014. The perils of peer effects. Labour Economics, 30, pp.98-108.
Arduini, T., Patacchini, E. and Rainone, E., 2020. Identification and estimation of network models with heterogeneous interactions. In The Econometrics of Networks. Emerald Publishing Limited.
Arduini, T., Patacchini, E. and Rainone, E., 2020. Treatment effects with heterogeneous externalities. Journal of Business & Economic Statistics, 38(4), pp.826-838.
Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. The effect of classmate characteristics on post-secondary outcomes: Evidence from the Add Health. American Economic Journal: Economic Policy 3.1 (2011): 25-53.
Johnsson, I. and Moon, H.R., 2021. Estimation of peer effects in endogenous social networks: Control function approach. Review of Economics and Statistics, 103(2), pp.328-345.
Teaching methods
Classroom Lectures;
Laboratory sessions, Exercises/workouts
Workshops and students discussions on specific projects works individually assigned
Lessons focused on problem-solving
Searching of additional dedicated materials in the library and on-line
Analysis of referred readings and textbooks
Suggested articles and readings
Laboratory sessions, Exercises/workouts
Workshops and students discussions on specific projects works individually assigned
Lessons focused on problem-solving
Searching of additional dedicated materials in the library and on-line
Analysis of referred readings and textbooks
Suggested articles and readings
Exam Rules
The assessment will be based on
- replication study (70%; max 23 out of 33 points)
Students will have to submit: a) a R/Stata code to replicate the results of a published paper that uses at least one of the tools students learnt ; b) a Fact checking report, summarizing the results of the replication study (guidelines to be illustrated in class); c) slides on an extension of the replicated study
- in class participation (20%; max 7 out of 33 points)
- replication study (70%; max 23 out of 33 points)
Students will have to submit: a) a R/Stata code to replicate the results of a published paper that uses at least one of the tools students learnt ; b) a Fact checking report, summarizing the results of the replication study (guidelines to be illustrated in class); c) slides on an extension of the replicated study
- in class participation (20%; max 7 out of 33 points)