Identification & Estimation of Network Statistics with Missing Link Data: Matthew Thirkettle

Matthew Thirkettle (Rice) will present "Identification & Estimation of Network Statistics with Missing Link Data".

Abstract: I obtain informative bounds on network statistics in a partially observed network whose formation I explicitly model. Partially observed networks are commonplace due to, for example, partial sampling or incomplete responses in surveys. Network statistics (e.g., centrality measures) are not point identified when the network is partially observed. Worst-case bounds on network statistics can be obtained by letting all missing links take values zero and one. I dramatically improve on the worst-case bounds by specifying a structural model for network formation. An important feature of the model is that I allow for positive externalities in the network-formation process. The networkformation model and network statistics are set identified due to multiplicity of equilibria. I provide a computationally tractable outer approximation of the joint identified region for preferences determining network-formation processes and network statistics. In a simulation study on Katz-Bonacich centrality, I find that worst-case bounds that do not use the network formation model are 44 times wider than the bounds I obtain from my procedure.

Read his full paper here.

Please contact Daniel Wilhelm at d.wilhelm@ucl.ac.uk for the seminar link.