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Valuing Structure, Model Uncertainty and Model Averaging in Vector Autoregressive Process

Author

Listed:
  • Rodney W Strachan

    (University of Leicester)

  • Herman K van Dijik

    (Erasmus University Rotterdam)

Abstract
Economic policy decisions are often informed by empirical analysis based on accurate econometric modeling. However, a decision-maker is usually only interested in good estimates of outcomes, while an analyst must also be interested in estimating the model. Accurate inference on structural features of a model improves policy analysis as it improves estimation, inference and forecast efficiency. In this paper a Bayesian inferential procedure is presented which allows for unconditional inference on structural features of vector autoregressive (VAR) processes. We employ measures on manifolds in order to elicit uniform priors on subspaces defined by particular structural features of VARs. The features considered are cointegration, exogeneity, deterministic processes and overidentification. Posterior probabilities of these features are used in a model averaging approach for forecasting and impulse response analysis. The methods are applied to three empirical economic issues: stability of Australian money demand; relative weights of permanent and transitory shocks in a US real business cycle model; and possible evidence on an inflationary oil price shock and a liquidity trap in a UK macroeconomic model. The results obtained illustrate the feasibility of the proposed methods.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Rodney W Strachan & Herman K van Dijik, 2005. "Valuing Structure, Model Uncertainty and Model Averaging in Vector Autoregressive Process," Money Macro and Finance (MMF) Research Group Conference 2005 30, Money Macro and Finance Research Group.
    • Handle: RePEc:mmf:mmfc05:30
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    Citations

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    Cited by:

    1. Hoogerheide, Lennart F. & Kaashoek, Johan F. & van Dijk, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," Journal of Econometrics, Elsevier, vol. 139(1), pages 154-180, July.
    2. Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2008. "Bayesian inference in a cointegrating panel data model," Advances in Econometrics, in: Bayesian Econometrics, pages 433-469, Emerald Group Publishing Limited.
    3. Andrea Silvestrini, 2010. "Testing fiscal sustainability in Poland: a Bayesian analysis of cointegration," Empirical Economics, Springer, vol. 39(1), pages 241-274, August.
    4. Rodney W. Strachan & Herman K. van Dijk, 2014. "Divergent Priors and Well Behaved Bayes Factors," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(1), pages 1-31, March.
    5. Gary Koop & Rodney Strachan & Herman van Dijk & Mattias Villani, 2004. "Bayesian Approaches to Cointegration," Discussion Papers in Economics 04/27, Division of Economics, School of Business, University of Leicester.
    6. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2019. "Priors for the Long Run," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 565-580, April.
    7. David Ardia & Lukasz T. Gatarek & Lennart Hoogerheide & Herman K. Van Dijk, 2016. "Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices," Econometrics, MDPI, vol. 4(1), pages 1-19, March.
    8. Ciobotaru, Corina & Mazza, Christian, 2022. "Consistency and asymptotic normality of M-estimates of scatter on Grassmann manifolds," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    9. Radchenko, Stanislav & Tsurumi, Hiroki, 2006. "Limited information Bayesian analysis of a simultaneous equation with an autocorrelated error term and its application to the U.S. gasoline market," Journal of Econometrics, Elsevier, vol. 133(1), pages 31-49, July.
    10. Villani, Mattias, 2005. "Bayesian Inference of General Linear Restrictions on the Cointegration Space," Working Paper Series 189, Sveriges Riksbank (Central Bank of Sweden).
    11. repec:onb:oenbwp:y::i:164:b:1 is not listed on IDEAS
    12. Gary Koop & Roberto León-González & Rodney W. Strachan, 2010. "Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 224-242, April.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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