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12:30 - 13:30 24 November 2015

Bayesian Indirect Inference and the ABC of GMM

Location

IFS Seminar Room | Institute for Fiscal Studies (link Map)
7 Ridgmount Street | London | WC1E 7AE | United Kingdom

Open to: Academic | Student
Ticketing: Open

Speaker information

Han Hong, Stanford

In this paper we propose and study local linear and polynomial based estimators for implementing ABC style computation of indirect inference and GMM estimators. This method makes use of nonparametric regression in the computation of GMM and Indirect Inference models. We provide formal conditions under which frequentist inference is asymptotically valid and demonstrate the validity of the estimated posterior quantiles for confidence interval construction. We also show that in this setting, local linear kernel regression methods have theoretical advantages over local constant kernel methods that are also reflected in finite sample simulation results. Our results also apply to both exactly and over identified models. These estimators do not need to rely on numerical optimization or Markov Chain Monte Carlo simulations.


Contact

Institute for Fiscal Studies
020 7291 4800


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