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Bayesian Analysis of Stochastic and Deterministic Processes in The Error Correction Model

Author

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  • Strachan, Rodney

    (University of Liverpool)

  • Brett Inder

Abstract

In this article a method for joint estimation of the number of stochastic trends and the deterministic processes in a multivariate error correction model is presented. This approach takes advantage of the Laplace method of approximating integrals and, the second important contribution of the paper, careful elicitation of the prior for the cointegrating vectors from a prior on the cointegrating space. The approach follows the classical approaches of James (1969), Anderson (1951) and Johansen (1988 and 1991) and performs well when used to estimate the number of stochastic trends compared with information criteria in finite samples in Monte Carlo experiments.

Suggested Citation

  • Strachan, Rodney & Brett Inder, 2003. "Bayesian Analysis of Stochastic and Deterministic Processes in The Error Correction Model," Royal Economic Society Annual Conference 2003 197, Royal Economic Society.
    • Handle: RePEc:ecj:ac2003:197
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    More about this item

    Keywords

    stochastic trend; deterministic trend; posterior probability; Grassman manifold; Stiefel manifold;
    All these keywords.

    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

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