Talk:Cointegration

message: A stock price and the dividend yield are not going to be cointegrated unless the dividend sequence is a stationary series.  I would consider this to be quite unlikely from simple casual empiricism.
On the other hand, the basis on a stock futures contract would most likely be stationary.  Feel free to revert the topic if you like but I think I'm right here.— Preceding unsigned comment added by 217.158.177.18 (talk) 18:17, 23 May 2005
:Hi,
: Thanks for contributing. You will acknowledge that the hypothesis of dividend->price connection could be (and has been) tested using cointegration techniques (whether or not they really are cointegrated). I will never revert an edit, especially a constructive one like this. 
: You may well have spotted an important error in communicating the idea, in that dividends and prices are not obviously cointegrated. However, I would say that futures and spot prices are so clearly cointegrated that we create another source of confusion; people could ask themselves, what the value of the method is (if it can only be used to prove relationships which are already enforced by arbitrage). I think the ideal example(s) would be less obvious than price-future, but more obvious than price-dividend. 
: What we really need is a case in which cointegration techniques proved their value against earlier econometrics, in an easy to describe way.
::Any ideas?
Wragge 19:47, 2005 May 23 (UTC)
:1st sentence: it's not a "technique" any more than correlation is a technique.  it's a "property" ... where the difference between two non-stationary series may itself be stationary. Derex 22:26, 18 January 2006 (UTC)
thread_title: Untitled

message: From the intro:
"Cointegration is a statistical property of a collection (X1,X2,...,Xk) of time series variables. First, all of the series must be integrated of order 1 (see Order of Integration). Next, if a linear combination of this collection is integrated of order zero, then the collection is said to be co-integrated. Formally, if (X,Y,Z) are each integrated of order 1, and there exist coefficients a,b,c such that aX+bY+cZ is integrated of order 0, then X,Y, and Z are cointegrated."
From the actual Engle & Granger paper:
 "DEFINITION: The components of the vector xt are said to be co-integrated of order d, b, denoted xt ~ CI(d, b), if (i) all components of xt are I(d); (ii) there exists a vector a !=0 so that zt=a'xt-I(d -b), b>0. The vector a is called the co-integrating vector."
Nowhere in here, Engle & Granger make a statement  that "all of the series must be integrated of order 1 ". It is possible to have cointegration with higher order of integration as well.  — Preceding unsigned comment added by 132.230.239.137 (talk) 14:35, 15 May 2016 (UTC)
thread_title:  I(1) 

message: I wonder about the line "Before the 1980s many economists used linear regressions on (de-trended) non-stationary time series data, ... showed to be a dangerous approach ..." Doesn't spurious correlation arise when you are using non-stationary data? If they used de-trended data then their data would be stationary (even if they de-trended it erroneously); thus, cannot be spuriously correlated, just badly correlated. However, if Granger says so, then I will definitely take his word for it. Therefore, I would like a reference on the word "de-trended". Ikiwi (talk) 13:25, 7 June 2011 (UTC)
replies: text: No, if they detrended the data erroneously (and simply using the residuals from a regression of the data on time is sometimes erroneous) then the detrended data are not stationary. That's what is meant by erroneously in this context. That is the criticism that gave rise to cointegration tests.  Duoduoduo (talk) 20:31, 6 January 2013 (UTC)
thread_title:  De-trended? 

message: This article makes it sounds like cointelation is a widely accepted alternative to cointegration. However, the only reference I can find to cointelation is the 2012 article that is cited in this wikipedia page. Did the authors of the 2012 cointelation article update this part of the wiki themselves? Since this method is not widely used and does not constitute a major advance in econometrics, I highly doubt it belongs on the cointegration wiki page.  — Preceding unsigned comment added by 18.111.26.23 (talk) 20:23, 17 February 2013 (UTC)
replies: text:  Yeah. Never heard of it either. Does not appear in any notable journal of statistics, econometrics, or mathematics. Typical Wikipedia cra*.  — Preceding unsigned comment added by 83.150.88.127 (talk) 12:43, 12 May 2013 (UTC)
replies: text:  Agreed 94.100.23.163 (talk) 07:53, 30 May 2013 (UTC)
thread_title:  Cointelation? 

message: Made some major edits, moving most things about and hopefully clarifying things. I'm not an econometrician, but I have a) some mid-level stats training and b) some experience teaching stats to people who are unfamiliar with them. Theblindsage (talk) 22:30, 5 May 2015 (UTC)
thread_title:  Major Edit 

message: I have deleted this comment from the last part of HISTORY and replaced by more accurate description:
—even though there is no direct relationship between the two series and hence the high correlation between the two series is probably caused by ″global″ economic factors that affect both countries, an uncareful reading of the regression results could lead to the misinterpretation of a strong direct relationship between the two series. The R-square will typically be much lower if we regress across the time series of returns between one Fiji consumption level and the next (verus returns in the Afgan GNP), but subtle spurious regression may remain.
This passage reflects the common misunderstanding that if there is correlation it must be for a cause -- if the two series are not related than there is some global economic factor which makes them appear related. SPURRIOUS means that there is a correlation but there is no cause -- that is why it is spurious.Asaduzaman (talk) 14:10, 24 October 2015 (UTC)
thread_title:  Common Confusion 

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