The paper aims at developing the Bayesian seasonally cointegrated model for quarterly data. We propose the prior structure, derive the set of full conditional posterior distributions, and propose the sampling scheme. The identification of cointegrating spaces is obtained \emph{via} orthonormality restrictions imposed on vectors spanning them. In the case of annual frequency, the cointegrating vectors are complex, which should be taken into account when identifying them. The point estimation of the cointegrating spaces is also discussed. The presented methods are illustrated by a simulation experiment and are employed in the analysis of money and prices in the Polish economy.
翻译:本文旨在为季度数据开发巴伊西亚季节性联合模型,我们提出先前的结构,提出一整套有条件的后座分布,并提议取样办法;确定对横跨这些空间的矢量的混合空间。如果是每年的频率,融合矢量是复杂的,在确定它们时应该加以考虑。还讨论对融合空间的点估计。提出的方法通过模拟试验加以说明,并用于分析波兰经济的货币和价格。