Steven Durlauf - University of Wisconsin-Madison
CANCELLED
Date: 11/16/2011 (Wed)
Time: 3:30pm- 5:00pm
Location: Seminar will be held on-site: Social Sciences 111
Organizer: V. Joseph Hotz
Meeting Schedule: Login or email the organizer to schedule a meeting.
All meetings will be held in the same location as the seminar unless otherwise noted.
9:00am - Breakfast with Grad Students (Ed Kung, Dan LaFave, Ralph Mastromonaco, Deborah Rho, Teresa Romano) at Washington Duke Inn
10:00am - Joe Hotz (319 Soc. Sci. Bldg.)
10:30am - Duncan Thomas (319 Soc. Sci. Bldg.)
11:00am - Arnaud Maurel (319 Soc. Sci. Bldg.)
11:30am - Alessandro Tarozzi (319 Soc. Sci. Bldg.)
12:00pm - Lunch: Jim Moody and colleagues from Duke Network Analysis Center (DNAC)
1:00pm - Pat Bayer (213 Social Sciences)
1:30pm - Hugh Macartney (319 Soc. Sci. Bldg.)
2:00pm - Marjorie McElroy (319 Soc. Sci. Bldg.)
2:30pm - Daniel Yi Xu (319 Soc. Sci. Bldg.)
3:00pm - Open
3:30pm - Seminar Presentation (3:30pm to 5:00pm)
5:30pm - Open
6:00pm - Taxicab pick-up in front of Duke Chapel for trip to RDU
Additional Comments: Abstract: This paper provides a systematic analysis of identification in linear social networks models. This is both a theoretical and an econometric exercise in that it links identification analysis to a rigorously delineated model of interdependent decisions. We develop a Bayes-Nash equilibrium analysis for interdependent decisions under incomplete information in networks that produces linear strategy profiles of the type conventionally used in empirical work and which nests linear social interactions models as a special case. Abstract: We consider identification of both contextual and endogenous social effects under alternative assumptions on the a priori information on network structure available to an analyst and contrast the informational content of individual-level and aggregated data. This analysis is then extended to an example of a two stage game in which networks form in the first stage and outcomes occur in the second. The effects of endogenous network formation on identification are then analyzed.