Personal profile
Biography
Personal Website: https://dtfrazier.netlify.com/
Research interests
Simulation-based inference in both the classical and Bayesian paradigm.
Financial Econometrics, with applications to asset pricing.
Nonparametric and semiparametric econometric modeling.
Monash teaching commitment
ETC 1010: Data Modeling and Computing
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 2 Zero Hunger -
SDG 3 Good Health and Well-being -
SDG 5 Gender Equality -
SDG 16 Peace, Justice and Strong Institutions
Collaborations and top research areas from the last five years
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Controlling Feedback in Big Multi-Module Statistical and Econometric Models
Smith, M. S. (Primary Chief Investigator (PCI)), Nott, D. J. (Partner Investigator (PI)) & Frazier, D. (Chief Investigator (CI))
ARC - Australian Research Council
1/07/25 → 30/06/28
Project: Research
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Loss-based Bayesian Prediction
Maneesoonthorn, O. (Primary Chief Investigator (PCI)), Martin, G. (Chief Investigator (CI)), Frazier, D. (Chief Investigator (CI)) & Hyndman, R. (Chief Investigator (CI))
19/06/20 → 18/06/26
Project: Research
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Consequences of Model Misspecification in Approximate Bayesian Computation
Frazier, D. (Primary Chief Investigator (PCI))
ARC - Australian Research Council
1/02/20 → 30/06/25
Project: Research
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The Validation of Approximate Bayesian Computation: Theory and Practice
Martin, G. (Primary Chief Investigator (PCI)), Frazier, D. (Chief Investigator (CI)), Renault, E. (Chief Investigator (CI)) & Robert, C. (Partner Investigator (PI))
ARC - Australian Research Council, Monash University, Brown University, Université Paris Dauphine (Paris Dauphine University)
1/02/17 → 31/12/21
Project: Research
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ABC-based forecasting in misspecified state space models
Weerasinghe, C., Loaiza-Maya, R., Martin, G. M. & Frazier, D. T., Jan 2025, In: International Journal of Forecasting. 41, 1, p. 270-289 20 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus) -
Decomposing identification gains and evaluating instrument identification power for partially identified average treatment effects
Zhang, L., Frazier, D. T., Poskitt, D. S. & Zhao, X., 2025, In: Econometric Reviews. 44, 7, p. 915-938 24 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile -
Loss-based variational Bayes prediction
Frazier, D. T., Loaiza-Maya, R., Martin, G. M. & Koo, B., 2025, In: Journal of Computational and Graphical Statistics. 34, 1, p. 84-95 12 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile4 Link opens in a new tab Citations (Scopus) -
Synthetic likelihood in misspecified models
Frazier, D. T., Nott, D. J. & Drovandi, C., 2025, In: Journal of the American Statistical Association. 125, 550, p. 884-895 13 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile -
Weak identification in discrete choice models
Frazier, D. T., Renault, E., Zhang, L. & Zhao, X., Mar 2025, In: Journal of Econometrics. 248, 19 p., 105866.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus)