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Approximate Bayes Projects
David T. Frazier
DECRA Fellow and Associate Professor in Econometrics and Statistics
My research interests include simulation-based statistical theory and inference, approximate Bayesian analysis, forecasting and scoring rules.
Publications
Model Misspecification in ABC (Consequences and Diagnostics)
We analyze the behavior of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual …
Indirect Inference for Locally Stationary Models
We propose the use of indirect inference estimation for inference in locally stationary models. We develop a local indirect inference …
Approximate Bayesian Forecasting
Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of …
Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models
A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC …
Asymptotic Properties of Approximate Bayesian Computation
Approximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider …
