In the lecture, the equation to find half life is given as log(2)/theta where theta is the eigenvalue of the Johansen test. However, in the coding example that follows the half life lecture, the value of theta was derived from a CADF as opposed to taking it from a Johansen test and it is obtained by running a linear regression on the difference between mean of spread and spread, and the difference between tomorrow's value of spread and today's value of spread.
Based on the above, I have two questions which I am hoping to get some help on:
1.) what is the code to get the eigenvalue attributes from the Johansen test result
2.) can someone explain the logic in the method above to obtain the eigenvalue from a CADF?
Many thanks in advance,
Ryan
Hello Ryan, thanks for your question.
1) In the notebook, you can get the eigenvalue using the code:
# Store the results of Johansen test
result = coint_johansen(df[:90], 0, 1)
# Store the value of eigenvector. Using this eigenvector, you can create the spread
ev = result.evec
2) The CADF returns a t-statistic and not an eigenvalue.