Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes
AbstractEconomic 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 06/5.
Date of creation: Feb 2006
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Postal: Department of Economics University of Leicester, University Road. Leicester. LE1 7RH. UK
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Other versions of this item:
- Strachan, R.W. & van Dijk, H.K., 2006. "Model uncertainty and Bayesian model averaging in vector autoregressive processes," Econometric Institute Research Papers EI 2006-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- 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
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-03-05 (All new papers)
- NEP-ECM-2006-03-05 (Econometrics)
- NEP-ETS-2006-03-05 (Econometric Time Series)
- NEP-FOR-2006-03-05 (Forecasting)
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- Stanislav Radchenko, 2004.
"Limited Information Bayesian Analysis of a Simultaneous Equation with an Autocorrelated Error Term and its Application to the U.S. Gasoline Market,"
- 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.
- Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006.
"Econometrics: A Bird’s Eye View,"
Cambridge Working Papers in Economics
0655, Faculty of Economics, University of Cambridge.
- Geweke, John F. & Horowitz, Joel L. & Pesaran, M. Hashem, 2006. "Econometrics: A Bird's Eye View," IZA Discussion Papers 2458, Institute for the Study of Labor (IZA).
- John Geweke & Joel Horowitz & M. Hashem Pesaran, 2006. "Econometrics: A Bird’s Eye View," CESifo Working Paper Series 1870, CESifo Group Munich.
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