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Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes

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  • Rodney W. Strachan

    ()

  • Herman K. van Dijk

Abstract

Economic forecasts and policy decisions are often informed by empirical analysis based on econometric models. However, inference based upon a single model, when several viable models exist, limits its usefulness. Taking account of model uncertainty, a Bayesian model averaging procedure is presented which allows for unconditional inference within the class of vector autoregressive (VAR) processes. Several features of VAR process are investigated. Measures on manifolds are employed in order to elicit uniform priors on subspaces defined by particular structural features of VARs. The features considered are the number and form of the equilibrium economic relations and deterministic processes. Posterior probabilities of these features are used in a model averaging approach for forecasting and impulse response analysis. The methods are applied to investigate stability of the "Great Ratios" in U.S. consumption, investment and income, and the presence and effects of permanent shocks in these series. The results obtained indicate the feasibility of the proposed method.

Suggested Citation

  • Rodney W. Strachan & Herman K. van Dijk, 2006. "Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes," Discussion Papers in Economics 06/5, Department of Economics, University of Leicester.
  • Handle: RePEc:lec:leecon:06/5
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    File URL: http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp06-5.pdf
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    References listed on IDEAS

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    1. Fernando Galindo-Rueda & Anna Vignoles, 2005. "The Declining Relative Importance of Ability in Predicting Educational Attainment," Journal of Human Resources, University of Wisconsin Press, vol. 40(2).
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    Cited by:

    1. 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.
    2. John Geweke & Joel Horowitz & M. Hashem Pesaran, 2006. "Econometrics: A Bird’s Eye View," CESifo Working Paper Series 1870, CESifo Group Munich.

    More about this item

    Keywords

    Posterior probability; Grassman manifold; Orthogonal group; Cointegration; Model averaging; Stochastic trend; Impulse response; Vector autoregressive model.;

    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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