Structural Error Correction Model: A Bayesian Perspective
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 parametersâ€™ pdfs cover the actual values. Key words: structural error correction model
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