Thanks to Mat, Charles and Eric for all the comments! and for directing me
to relevant literature which are extremely useful, it's certainly worth
reading the papers and understanding a bit more about the MLE properties in
Johansen test.

Stability of the estimates is very important to me, and Johansen approach
seems cannot guarantee that, I will try something esle (perhaps DOLS) and
see how it goes.

Again, thanks very much for the help.

Best,

Paul

2010/12/4 mat <matthieu.stigler@gmail.com>

> Depending on what the goal of the analysis is, but if the goal is to
> examine parameter stability, there is a recent nice paper that allows "time
> varying" cointegration (estimated by the Johansen ML):
>
> http://ideas.repec.org/a/cup/etheor/v26y2010i05p1453-1490_99.html
>
> I used this method, and the code will be available (hopefully) soon in
> package tsDyn.
>
> Best
>
> Matthieu
>
> Le 03. 12. 10 18:26, Eric Zivot a écrit :
>
> The situation is even worse, because rolling estimates are correlated as
>> well. Because the finite sample distn has no moments, you expect to see
>> crazy results from the Johansen MLE from time to time. I have certainly seen
>> estimates that are 10,000 when they should be near 1. This of course, makes
>> it very difficult to judge the stability of recursive or rolling MLEs.
>> Because the Stock-Watson DOLS estimates are regression they will be more
>> stable. Still it would be nice to have some distribution theory for moving
>> estimates of a cointegrating vector. Banerjee, Lumsdain and Stock has a JBES
>> 1992 paper that derived the distn of rolling and recursive unit root tests.
>> It would be nice to know if such an analysis has been done for
>> cointegration.
>>
>> Eric Zivot
>> Robert Richards Chaired Professor of Economics
>> Adjunct Professor of Finance
>> Adjunct Professor of Statistics
>> Department of Economics
>> Box 353330                  email:  ezivot@u.washington.edu
>> University of Washington    phone:  206-543-6715
>> Seattle, WA 98195-3330
>>                                               www:
>> http://faculty.washington.edu/ezivot
>>
>>
>>
>> -----Original Message-----
>> From: r-sig-finance-bounces@r-project.org [mailto:
>> r-sig-finance-bounces@r-project.org] On Behalf Of mat
>> Sent: Friday, December 03, 2010 2:53 AM
>> To: r-sig-finance@r-project.org
>> Subject: Re: [R-SIG-Finance] unstable cointegration vector estimates in
>> Johansen test
>>
>> Hi
>>
>> Besides the problem of different results based on different estimators,
>> the fact that when doing a rolling analysis you find very different
>> values with the same estimator could be due to a strange finite sample
>> property of the ML estimator. Philips (ref below) finds indeed that in
>> small samples the ML estimator has no finite moments, what can explain
>> that single value have important impacts.
>>
>> See:
>>
>> Phillips, Peter C B, 1994.
>> "Some Exact Distribution Theory for Maximum Likelihood Estimators of
>> Cointegrating Coefficients in Error Correction Models,"
>> Econometrica, Econometric Society, vol. 62(1), pages 73-93, January.
>>
>> http://ideas.repec.org/a/ecm/emetrp/v62y1994i1p73-93.html
>>
>> Best
>>
>> Matthieu
>>
>>
>>
>> Le 02. 12. 10 16:33, Charles Evans a écrit :
>>
>>
>>> Hello Paul (Lestat?!?),
>>>
>>> In my work, I have looked at potential cointegration between certain
>>> categories of ETFs and the most nearly related futures, and I have
>>> found wide disagreement among different cointegration tests.
>>>
>>> Different cointegration tests have different strengths and
>>> weaknesses.  Because there is rarely a conclusive reason to prefer any
>>> particular cointegration test over all others.  Optimally, one would
>>> use a variety (e.g., Engle-Granger/ADF, Engle-Granger/Phillips-Perron,
>>> ECM, Phillips-Ouliaris [ca.po], Johansen [ca.jo]) and run with the
>>> consensus.  Although it could be a bit tedious, you could run your
>>> rolling cointegration tests using each of these and see if you get the
>>> same odd behavior consistently.  If you do, then that would suggest
>>> something interesting; if not, then it could just be an artifact of
>>> the specific test.
>>>
>>> If you have access to statistics and econometrics journals, you might
>>> find these papers helpful:
>>>
>>> Gregory, Allan W., Alfred A. Haug, and Nicoletta Lomuto, 2004, Mixed
>>> signals among tests for cointegration. Journal of Applied Econometrics
>>> 19 (1), 89-98.
>>>
>>> Hanck, Christoph, 2007, Mixed signals among panel cointegration tests.
>>> Working Paper.
>>>
>>> https://editorialexpress.com/cgibin/conference/download.cgi?db_name=sce2007&paper_id=115
>>>
>>>
>>> Haug, Alfred A., 1996, Tests for cointegration: A Monte Carlo
>>> comparison. Journal of Econometrics 71, 89-115.
>>>
>>> Östermark, Ralf and Rune Höglund, 2000. Monte Carlo tests of
>>> cointegration with structural breaks. Kybernetes 29 (9/10), 1284-1297.
>>>
>>> Östermark, Ralf and Rune Höglund, 1999. Simulating competing
>>> cointegration tests in a bivariate system. Journal of Applied
>>> Statistics 27 (7), 831-846.
>>>
>>> HTH,
>>>
>>> Charles Evans
>>> cevans@chyden.net
>>>
>>> No one ever says, "First shoot all the plumbers."
>>> Steve Foerster
>>>
>>> On 1 Dec 2010, at 5:45 PM, 金陈琛 wrote:
>>>
>>>
>>>
>>>> Hi, all,
>>>>
>>>> a question regarding the cointegration relations (vectors) estimate
>>>> in the
>>>> Johansen test:
>>>>
>>>> I have a data sample which is confirmed by the Johansen test as
>>>> cointegrated, however, if I take a subsample of the whole times
>>>> series, each
>>>> time add one data point and using the "ca.jo" function in R to
>>>> estimate the
>>>> cointegrating vector, i.e. to do a forward recursive test, and record
>>>> the
>>>> estimate beta (the first element of the vector is normalized to one,
>>>> beta is
>>>> the second element), strangely at some point, beta shows
>>>> discontinuity-a big
>>>> jump with sign change. This is really confusing to me as it seems
>>>> that the
>>>> Johansen procedure is not robust in that one additional data could cause
>>>> dramatical change in the estimate of beta. not sure if it is problem
>>>> of the
>>>> procedure or the ca.jo function, I think my data is fine (excluding
>>>> errors
>>>> and outliers). has anyone seen similar things as me?
>>>>
>>>> Regards,
>>>> Paul C. Jin
>>>>
>>>>     [[alternative HTML version deleted]]
>>>>
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>>>>
>>> _______________________________________________
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>>>
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-- 
Jin Chenchen

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