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Quasi-Bayesian estimation of time-varying volatility in DSGE models

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Date
01/2019
Author
Petrova, Katerina
Keywords
Time-varying volatility
DSGE models
Bayesian methods
HB Economic Theory
NDAS
BDC
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Abstract
We propose a novel quasi‐Bayesian Metropolis‐within‐Gibbs algorithm that can be used to estimate drifts in the shock volatilities of a linearized dynamic stochastic general equilibrium (DSGE) model. The resulting volatility estimates differ from the existing approaches in two ways. First, the time variation enters non‐parametrically, so that our approach ensures consistent estimation in a wide class of processes, thereby eliminating the need to specify the volatility law of motion and alleviating the risk of invalid inference due to mis‐specification. Second, the conditional quasi‐posterior of the drifting volatilities is available in closed form, which makes inference straightforward and simplifies existing algorithms. We apply our estimation procedure to a standard DSGE model and find that the estimated volatility paths are smoother compared to alternative stochastic volatility estimates. Moreover, we demonstrate that our procedure can deliver statistically significant improvements to the density forecasts of the DSGE model compared to alternative methods.
Citation
Petrova , K 2019 , ' Quasi-Bayesian estimation of time-varying volatility in DSGE models ' , Journal of Time Series Analysis , vol. 40 , no. 1 , pp. 151-157 . https://doi.org/10.1111/jtsa.12290
Publication
Journal of Time Series Analysis
Status
Peer reviewed
DOI
https://doi.org/10.1111/jtsa.12290
ISSN
0143-9782
Type
Journal article
Rights
Copyright © 2018, John Wiley & Sons Ltd. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1111/jtsa.12290
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/17421

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