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Structural Error Correction Model: A Bayesian Perspective

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Author Info
Chew Lian Chua
Peter Summers

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Abstract

This paper proposes a Structural Error Correction Model (SECM) that allows concurrent estimation of the structural parameters and analysis of cointegration. We amalgamate the Bayesian methods of Kleibergen and Paap (2002) for analysis of cointegration in the ECM, and the Bayesian methods of Waggoner and Zha (2003) for estimating the structural parameters in BSVAR into our proposed model. Empirically, we apply the SCEM to four data generating processes, each with a different number of cointegrating vector. The results show that in each of the DGPs, the Bayes factors are able to select the appropriate cointegrating vectors and the estimated marginal posterior parameters’ pdfs cover the actual values. Key words: structural error correction model

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Paper provided by Econometric Society in its series Econometric Society 2004 Far Eastern Meetings with number 702.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:feam04:702

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Related research
Keywords: structural error correction model; cointegration; Bayesian; structural parameters; singular value decomposition.;

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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  1. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March. [Downloadable!] (restricted)
  2. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May. [Downloadable!] (restricted)
  3. Kleibergen, F.R. & Van Dijk, H.K., 1993. "On the Shape of the Likelyhood/Posterior in Cointegration Models," Papers 9315-a, Erasmus University of Rotterdam - Econometric Institute.
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  4. Strachan, Rodney W, 2003. "Valid Bayesian Estimation of the Cointegrating Error Correction Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 185-95, January.
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  5. BAUWENSÊLuc, GIOTÊPierre,, 1997. "A Gibbs sampling approach to cointegration," CORE Discussion Papers 1997016, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
  6. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(1996-2), pages 1-78. [Downloadable!]
  7. Sugita, K., 2001. "Bayesian Cointegration Analysis," The Warwick Economics Research Paper Series (TWERPS) 591, University of Warwick, Department of Economics.
  8. Amisano, Gianni, 2003. "Bayesian inference in cointegrated systems," Research in Economics, Elsevier, vol. 57(4), pages 287-314, December. [Downloadable!] (restricted)
  9. Kleibergen, Frank & Paap, Richard, 2002. "Priors, posteriors and bayes factors for a Bayesian analysis of cointegration," Journal of Econometrics, Elsevier, vol. 111(2), pages 223-249, December. [Downloadable!] (restricted)
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  10. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-68, November.
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