Bayesian model averaging in vector autoregressive processes with an investigation of stability of the US great ratios and risk of a liquidity trap in the USA, UK and Japan
AbstractA Bayesian model averaging procedure is presented within the class of vector autoregressive (VAR) processes and applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the presence and e¤ects of permanent shocks. Measures on manifolds are employed in order to elicit uniform priors on subspaces defned by particular structural features of linear VARs. Second, the VAR model is extended to include a smooth transition function in a (monetary) equation and stochastic volatility in the disturbances. The risk of a liquidity trap in the USA, UK and Japan is evaluated, together with the expected cost of a policy adjustment of central banks. Posterior probabilities of different models are evaluated using Markov chain Monte Carlo techniques.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2007-11.
Date of creation: 25 Mar 2007
Date of revision:
Grassman manifold; cointegration; great ratios; impulse response; liquidity trap; model averaging; orthogonal group; posterior probability; stochastic trend; vector autoregressive model;
Other versions of this item:
- Rodney Strachan & Herman K. van Dijk, . "Bayesian Model Averaging in Vector Autoregressive Processes with an Investigation of Stability of the US Great Ratios and Risk of a Liquidity Trap in the USA, UK and Japan," MRG Discussion Paper Series, School of Economics, University of Queensland, Australia 1407, School of Economics, University of Queensland, Australia.
- 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
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