Assessing the strength of parameter identification in empirically relevant DSGE Models - Which posterior sampling method performs best?

Invited for consideration for Advances in Econometrics Volume 45

Abstract

We show that weak identification is a serious concern in empirically relevant DSGE models with many nominal, real and financial frictions, as the likelihood/posterior functions of these models are typically not well behaved. To this end, we offer (1) a formal Bayesian approach by using a well-established indicator based on the posterior precision to detect weak identification and (2) a set of applied tips on how to cope with the estimation difficulties. We focus particularly on which posterior sampling method (RW-MH, TaRB-MH, Slice) to use and how to fine-tune it. We find that the slice sampler performs very well in terms of number of draws and computational time, and thus should be used when performing this strength of identification analysis before taking a model to data.

Date
Apr 16, 2020 — Apr 17, 2020
Location
Workshop on Bayesian Macroeconometric Modelling, Brisbane
Principal Investigator & PostDoc & Open-Source Enthusiast

My research interests include quantitative macroeconomics, econometrics and time series analysis.

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