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IF WE WRITE OUT A MODEL IT WILL GENERALLY CONTAIN MANY COINTEGRATING VECTORS (MAYBE ONE FOR EACH EQUATION).
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1. Lecture 6Stephen G HallMULTIVARIATE COINTEGRATION
12. SINGLE EQUATION TECHNIQUES CAN NEVER SATISFACTORILY ANSWER THESE POINTS.
JOHANSEN PROVIDES THE ANSWER
13. ESTIMATING MANY COINTEGRATING VECTORS
Johansen (1988) proposed a general framework for considering the possibility of multiple cointegrating vectors and this framework also allows questions of causality and general hypothesis tests to be carried out in a more satisfactory way.
Begin by defining a VAR of a set of variables X as
24. THEN A VALID COINTEGRATING VECTOR WILL PRODUCE A SIGNIFICANTLY NON-ZERO EIGENVALUE AND THE ESTIMATE OF THE COINTEGRATING VECTOR WILL BE GIVEN BY THE CORRESPONDING EIGENVECTOR.
25. THE COINTEGRATING SPACE
THE MATRIX OF EIGENVECTORS IS SAID TO SPAN THE COINTEGRATING SPACE THIS IS BECAUSE THEY ARE NOT THE SAME THING AS THE TARGET RELATIONSHIPS.
THE COINTEGRATING VECTORS MAY BE ANY LINEAR COMBINATION OF THE UNDERLYING TARGET RELATIONS. WE HAVE IN EFFECT ESTIMATED AN UNIDENTIFIED SYSTEM AND ALL WE REALLY KNOW ABOUT IS THE BOUNDARIES OF THE SPACE THAT THE TARGET RELATIONSHIPS MUST LIE WITHIN.
43. Conclusion
44. Example The Long Run Determination of the UK Monetary Aggregates
Hall, Henry and Wilcox
Bank of England Study
54. Similar results A similar set of results showing that log versions of the financial innovation variables also failed to cointegrate
55. So where now? Practitioners explain the result by financial innovation.
What drives this? Interest rates!
But summed over time because of learning
58. Seems to work! Cumulated interest rates seem to pick up the trend movement in velocity
59. Dynamic Model So we can go on and estimate a standard ECM based on this long run relationship
62. conclusion A parsimonious and very stable dynamic model
Cointegration has made an important missing effect very obvious
Fixing this improves things enormously