Search Results - subject:"approximate Bayesian computing"

Inference using simulated neural moments

Creel, Michael D. - In: Econometrics 9 (2021) 4, pp. 1-15

This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals...

Persistent link: https://www.econbiz.de/10012696340

Inference using simulated neural moments

Creel, Michael D. - In: Econometrics : open access journal 9 (2021) 4, pp. 1-15

Persistent link: https://www.econbiz.de/10012642418

Inference using simulated neural moments

Creel, Michael D. - 2020 - This version: November 2020

Persistent link: https://www.econbiz.de/10012431141

A note on Julia and MPI, with code examples

Creel, Michael D. - In: Computational economics 48 (2016) 3, pp. 535-546

Persistent link: https://www.econbiz.de/10011712534

Neural nets for indirect inference

Creel, Michael D. - 2016

Persistent link: https://www.econbiz.de/10011607329

Approximate Bayesian computing for spatial extremes

Erhardt, Robert J.; Smith, Richard L. - In: Computational Statistics & Data Analysis 56 (2012) 6, pp. 1468-1481

. In this paper, we present a Bayesian approach through the use of approximate Bayesian computing. This circumvents the … demonstrate that approximate Bayesian computing can provide estimates with a lower mean square error than the composite likelihood …

Persistent link: https://www.econbiz.de/10010871449