Bayesian Indirect Inference and the ABC of GMM
We propose and study local linear and polynomial based estimators for implementing Approximate Bayesian Computation (ABC) style 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 advantages over local constant kernel methods that are 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 (MCMC) simulations. They provide an effective complement to the classical M-estimators and to MCMC methods, and can be applied to both likelihood based models and method of moment based models.

Please sign up for meetings below:
docs.google.com/spreadsheets/d/1X58s71reMYccz52W0_cQ8wf5cUxvc4hOe2xJjjHkg3Q/edit#gid=0
Date: 18 January 2019, 14:15 (Friday, 1st week, Hilary 2019)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Seminar Room C
Speaker: Dennis Kristensen (UCL)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Melis Clark