Dr David Frazier
Lecturer
David Frazier is a 2020 Discovery Early Career Researcher Award (DECRA) fellow in the Department of Econometrics and Business Statistics at Monash University, working in the dual disciplines of econometrics and statistics. Frazier’s research interests are broad, but his primary focus has been on the area of simulation-based inference. Notably, he is one of only a handful of researchers to have made significant contributions to this field, which straddles the Bayesian and frequentist statistical paradigms. This includes novel methodological and theoretical developments in the realm of indirect inference and significant contributions to both the application of, and theoretical foundations for, approximate Bayesian computation (ABC). His most recent work has focussed on the challenging - but critically important - issue of model misspecification in simulation-based inference and forecasting; explorations that are really at the cutting edge of thinking in this field.
Research Interests:
Approximate Bayesian Computation
Asymptotic theory in Probability and Statistics
Nonparametric and Semiparametric Econometic Modelling
Simulation-based inference
Qualifications:
Ph.D. Economics, University of North Carolina
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
(2019). Focused Bayesian Prediction.
The 12th International Conference on Monte Carlo Methods & Applications.
Journal Articles
(In Press). Indirect inference for locally stationary models.
Journal of Econometrics. doi: 10.1016/j.jeconom.2020.08.004
(2021). A Note on the Accuracy of Variational Bayes in State Space Models: Inference and Prediction.
arXiv. arXiv:2106.12262v2.
(2021). Focused Bayesian prediction.
Journal of Applied Econometrics. 36(5), 517-543. doi: 10.1002/jae.2810
(2020). Robust Approximate Bayesian Computation: An Adjustment Approach.
arXiv. arXiv:2008.04099v1,
(2020). Optimal probabilistic forecasts: When do they work?.
arXiv. arXiv:2009.09592v1,
(2020). Computing Bayes: Bayesian Computation from 1763 to the 21st Century.
arXiv. arXiv:2004.06425v2,
(2019). Focused Bayesian Prediction.
arXiv. arXiv:1912.12571v1.
(2019). Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models.
Journal of Computational and Graphical Statistics. 28(3), 508-522. doi: 10.1080/10618600.2018.1552154
(2018). Indirect Inference with endogenously missing exogenous variables.
Journal of Econometrics. 205(1), 55 - 75. doi: 10.1016/j.jeconom.2018.03.005
(2018). Asymptotic properties of approximate Bayesian computation.
Biometrika. 105(3), 593 - 607. doi: 10.1093/biomet/asy027
(2018). A SIMPLE ITERATIVE Z-ESTIMATOR FOR SEMIPARAMETRIC MODELS.
Econometric Theory. 1 - 31. doi: 10.1017/S0266466618000063
