Approximate variational inference for a model of social interactions
AbstractThis paper proposes approximate variational inference methods for estimation of a strategic model of social interactions. Players interact in an exogenous network and sequentially choose a binary action. The utility of an action is a function of the choices of neighbors in the network. I prove that the interaction process can be represented as a potential game and it converges to a unique stationary equilibrium distribution. However, exact inference for this model is infeasible because of a computationally intractable likelihood, which cannot be evaluated even when there are few players. To overcome this problem, I propose variational approximations for the likelihood that allow approximate inference. This technique can be applied to any discrete exponential family, and therefore it is a general tool for inference in models with a large number of players. The methodology is illustrated with several simulated datasets and compared with MCMC methods.
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Bibliographic InfoPaper provided by NET Institute in its series Working Papers with number 13-16.
Length: 17 pages
Date of creation: Sep 2013
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Variational approximations; Bayesian Estimation; Social Interactions;
Find related papers by JEL classification:
- D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-10-05 (All new papers)
- NEP-ECM-2013-10-05 (Econometrics)
- NEP-GTH-2013-10-05 (Game Theory)
- NEP-UPT-2013-10-05 (Utility Models & Prospect Theory)
- NEP-URE-2013-10-05 (Urban & Real Estate Economics)
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