Forecasting with Bayesian Vector Autoregression

Karlsson, Sune

doi:10.1016/b978-0-444-62731-5.00015-4

Cited by 172 publications

(144 citation statements)

References 87 publications

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“…, ψ mk ) and conditional on H, the corresponding hyperparameters carry no additional information. The posterior mean α and variance V α take standard forms (Kadiyala et al, 1997;Karlsson, 2013),…”

Section: Posterior Distributionsmentioning

confidence: 99%

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Huber

Feldkircher

2017

Journal of Business & Economic Statistics

101

105

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Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this paper we derive the shrinkage prior of Griffin et al. (2010) for the VAR case and its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariances of the VAR along with Gamma priors on a set of local and global prior scaling parameters. This prior setup is then generalized by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. A simulation exercise shows that the proposed framework yields more precise estimates of the model parameters and impulse response functions. In addition, a forecasting exercise applied to US data shows that the proposed prior outperforms other specifications in terms of point and density predictions.

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Section: Posterior Distributionsmentioning

confidence: 99%

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Huber

Feldkircher

2017

Journal of Business & Economic Statistics

101

105

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“…If this likelihood is based on a distribution where marginalization can be handled analytically, then the marginalization problem of the predictive likelihood can be solved at this stage (see, e.g., Andersson and Karlsson, 2008, Karlsson, 2013, or Geweke and Amisano, 2010.…”

Section: Introductionmentioning

confidence: 99%

Marginalized Predictive Likelihood Comparisons of Linear Gaussian State-Space Models with Applications to DSGE, DSGE-VAR, and VAR Models

2014

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Abstract:The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.

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“…The posterior predictive density is naturally obtained as part of the Bayesian analysis and so is typically readily available to be used to form out-of-sample forecasts and for model evaluation (see Karlsson 2013 for a summary of the extensive Bayesian literature).…”

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confidence: 99%

Forecasting in Economics and Finance

Elliott

Timmermann

2016

Annu. Rev. Econ.

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Practices used to address economic forecasting problems have undergone substantial changes over recent years. We review how such changes have influenced the ways in which a range of forecasting questions are being addressed. We also discuss the promises and challenges arising from access to big data. Finally, we review empirical evidence and experience accumulated from the use of forecasting methods to a range of economic and financial variables.

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Forecasting with Bayesian Vector Autoregression

Cited by 172 publications

References 87 publications

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Marginalized Predictive Likelihood Comparisons of Linear Gaussian State-Space Models with Applications to DSGE, DSGE-VAR, and VAR Models

Forecasting in Economics and Finance

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