Abstract
We consider data where we have individuals affiliated with at most one organisational unit and where the interest is in modelling changes to these affiliations over time. This could be the case of people working for organisations or people living in neighbourhoods. We draw on dynamic models for social networks to propose an actor-oriented model for how these affiliations change over time. These models specifically take into account constraints of the system and allow for the system to be observed at discrete time-points. Constraints stem from the fact that for example not everybody can have the same job or live in the same neighbourhood, something which induces dependencies among the decisions marginally. The model encompasses two modelling components: a model for determining the termination of an affiliation; and a discrete-choice model for determining the new affiliation. For estimation we employ a Bayesian data-augmentation algorithm, that augments the observed states with unobserved sequences of transitions. We apply the proposed methods to a dataset of house-moves in Stockholm and illustrate how we may infer the mechanisms that sustain and perpetuate segregation on the housing market.
| Original language | English |
|---|---|
| Title of host publication | Studies in Theoretical and Applied Statistics |
| Editors | Cira Perna, Monica Pratesi, Anne Ruiz-Gazen |
| Publisher | Springer Nature |
| Pages | 107-117 |
| Volume | 227 |
| ISBN (Electronic) | 9783319739069 |
| ISBN (Print) | 9783319739052 |
| Publication status | Published - 2017 |
Keywords
- Social Networks
- Stochastic actor-oriented models
- Allocation models
- Matching models
Research Beacons, Institutes and Platforms
- Cathie Marsh Institute