Large Bayesian VARMAs

Chan, Joshua C. C.; Eisenstat, Eric; Koop, Gary

doi:10.1016/j.jeconom.2016.02.005

Cited by 38 publications

(58 citation statements)

References 29 publications

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“…Therefore, for larger systems one may wish to consider canonical specifications and follow the approach in Chan et al (2016). Therefore, for larger systems one may wish to consider canonical specifications and follow the approach in Chan et al (2016).…”

Section: Extension To the Varmamentioning

confidence: 99%

“…In Chan et al (2016), we develop algorithms that facilitate exact inference from echelon form VARMA specifications (with unknown Kronecker indices) and demonstrate that these can be readily used to estimate systems with as many as 12 equations. On this foundation, we have developed a straightforward Gibbs sampler for the model and discussed how this algorithm can be extended to models with time-varying VMA coefficients and stochastic volatility.…”

Section: Concluding Remarks and Future Researchmentioning

confidence: 99%

“…Identification based on the full rank of [A p ∶ Θ q ] assumption also becomes more difficult to justify as n increases. Therefore, for larger systems one may wish to consider canonical specifications and follow the approach in Chan et al (2016). A key point is that, regardless of the scheme used to uniquely identify A(L) and Θ(L), the expanded-form representation provides a convenient framework for developing sampling algorithms in VARMA specifications.…”

Section: Extension To the Varmamentioning

confidence: 99%

“…The methodology developed in this article leads to a few lines of inquiry in the future. In Chan et al (2016), we develop algorithms that facilitate exact inference from echelon form VARMA specifications (with unknown Kronecker indices) and demonstrate that these can be readily used to estimate systems with as many as 12 equations. An interesting result we obtain is that moving average components and the canonical form gain in importance as the system size increases, even when compared to parsimonious Bayesian VARs with shrinkage priors.…”

Section: Concluding Remarks and Future Researchmentioning

confidence: 99%

“…Note that inChan et al (2016) the focus is on large, constant-parameter VARMAs, whereas the main concern of this paper is small, time-varying parameter VARMAs.…”

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confidence: 99%

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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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Section: Extension To the Varmamentioning

confidence: 99%

Section: Concluding Remarks and Future Researchmentioning

confidence: 99%

Section: Extension To the Varmamentioning

confidence: 99%

Section: Concluding Remarks and Future Researchmentioning

confidence: 99%

“…Note that inChan et al (2016) the focus is on large, constant-parameter VARMAs, whereas the main concern of this paper is small, time-varying parameter VARMAs.…”

mentioning

confidence: 99%

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Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Chan

Eisenstat

2017

J of Applied Econometrics

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Large Bayesian Vector Autoregressions

Chan

2019

Macroeconomic Forecasting in the Era of Big Data

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Bayesian vector autoregressions are widely used for macroeconomic forecasting and structural analysis. Until recently, however, most empirical work had considered only small systems with a few variables due to parameter proliferation concern and computational limitations. We first review a variety of shrinkage priors that are useful for tackling the parameter proliferation problem in large Bayesian VARs, followed by a detailed discussion of efficient sampling methods for overcoming the computational problem. We then give an overview of some recent models that incorporate various important model features into conventional large Bayesian VARs, including stochastic volatility, non-Gaussian and serially correlated errors. Efficient estimation methods for fitting these more flexible models are also discussed. These models and methods are illustrated using a forecasting exercise that involves a real-time macroeconomic dataset. The corresponding MATLAB code is also provided. 1

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