Data-Based Priors for Vector Autoregressions with Drifting Coefficients

Korobilis, Dimitris

doi:10.2139/ssrn.2392028

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…Details are given in the online Appendix (supporting information). While Primiceri () is followed, it is noted that Korobilis () emphasizes the importance of priors for TVP‐VARs and finds that results can be sensitive to hyperparameters.…”

Section: Data and Priorsmentioning

confidence: 99%

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

Grant

2015

J of Applied Econometrics

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Summary This paper undertakes both a narrow and wide replication of the constant coefficients vector autoregression (VAR) identified with sign restrictions considered by Peersman (Journal of Applied Econometrics 2005; 20(2): 185–207. His results for the US are robust to an increase in the sample period from 2002:Q2 to 2014:Q2, but the extension to time‐varying parameters highlights the importance of testing the robustness of results against time variation. In particular, there are differences across models regarding the role of individual shocks during the 2001 US slowdown. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Data and Priorsmentioning

confidence: 99%

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

Grant

2015

J of Applied Econometrics

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“…Alternatively, hierarchical priors such as those in Korobilis (2014) can also be considered. Recall that the parameters 2 1 , … , 2 n are introduced to facilitate computation-for that purpose we will set the degree of freedom parameter ,0 to be small.…”

Section: Estimation Of Varma With Tvp and Svmentioning

confidence: 99%

“…In our application, this gamma prior works well. Alternatively, hierarchical priors such as those in Korobilis (2014) can also be considered.…”

Section: Estimation Of Varma With Tvp and Svmentioning

confidence: 99%

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Chan

Eisenstat

2017

J of Applied Econometrics

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Summary Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general vector autoregressive moving averages (VARMAs). A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with time‐varying vector moving average (VMA) coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.

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“…Methods such as those presented in Chan & Eisenstat (2017) could then possibly be adapted to estimate posterior odds. Korobilis (2014) estimates some prior parameters in a VAR with time-varying parameters and stochastic volatility. To be more specific, Korobilis (2014) restricts the prior covariance of the innovations to the parameters to be diagonal.…”

Section: Introductionmentioning

confidence: 99%

“…Korobilis (2014) estimates some prior parameters in a VAR with time-varying parameters and stochastic volatility. To be more specific, Korobilis (2014) restricts the prior covariance of the innovations to the parameters to be diagonal. Those diagonal elements are then estimated in a Gibbs sampling step.…”

Section: Introductionmentioning

confidence: 99%

Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

Amir-Ahmadi

Matthes

Wang

2018

Journal of Business & Economic Statistics

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Data-Based Priors for Vector Autoregressions with Drifting Coefficients

Cited by 11 publications

References 28 publications

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

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