Bayes Estimators of the Cointegration Space

Arbeitspapier

A neglected aspect of the otherwise fairly well developed Bayesian analysis of cointegration is the point estimation of the cointegration space. It is pointed out here that, due to the well known non-identification of the cointegration vectors, the parameter space is not an inner product space and conventional Bayes estimators therefore stand without their usual decision theoretic foundation. We present a Bayes estimator of the cointegration space which takes the curved geometry of the parameter space into account. Contrary to many of the Bayes estimators used in the literature, this estimator is invariant to the ordering of the time series. A dimension invariant overall measure of cointegration space uncertainty is also proposed. A small simulation study shows that the Bayes estimator compares favorably to the maximum likelihood estimator.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Sveriges Riksbank Working Paper Series ; No. 150

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Estimation: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: Bayesian inference ; Cointegration analysis ; Estimation ; Grassman manifold ; Subspaces.

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Villani, Mattias
• Event: Veröffentlichung
• (who): Sveriges Riksbank
• (where): Stockholm
• (when): 2003

Further information

• Handle: http://hdl.handle.net/10419/82441
• Last update: 10.03.2025, 11:43 AM CET