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Structural Error Correction Model: A Bayesian Perspective

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  • Chew Lian Chua
  • Peter Summers

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

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  1. Amisano, Gianni, 2003. "Bayesian inference in cointegrated systems," Research in Economics, Elsevier, vol. 57(4), pages 287-314, December.
  2. BAUWENS, Luc & GIOT, Pierre, 1997. "A Gibbs sampling approach to cointegration," CORE Discussion Papers 1997016, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Daniel F. Waggoner & Tao Zha, 2000. "A Gibbs simulator for restricted VAR models," Working Paper 2000-3, Federal Reserve Bank of Atlanta.
  4. 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.
  5. Sugita, K., 2001. "Bayesian Cointegration Analysis," The Warwick Economics Research Paper Series (TWERPS) 591, University of Warwick, Department of Economics.
  6. Kleibergen, Frank & van Dijk, Herman K., 1994. "On the Shape of the Likelihood/Posterior in Cointegration Models," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 514-551, August.
  7. Kleibergen, F.R. & Paap, R., 1998. "Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration," Econometric Institute Research Papers EI 9821, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  8. 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.
  9. 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(2), pages 1-78.
  10. Strachan, R., 2000. "Valid Bayesian Estimation of the Cointegrating Error Correction Model," Monash Econometrics and Business Statistics Working Papers 6/00, Monash University, Department of Econometrics and Business Statistics.
  11. Fisher, Lance A. & Huh, Hyeon-Seung & Summers, Peter M., 2000. "Structural Identification of Permanent Shocks in VEC Models: A Generalization," Journal of Macroeconomics, Elsevier, vol. 22(1), pages 53-68, January.
  12. 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.
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