Brian<br><br>Sorry for not answering your questions sooner. I appologise if this reply is a bit off topic for this mailing list.<br><br>I have attached two pdf's which I hope explain in some way how we used the Chow-Lin interpoalation/distribution methods. In the Central Bank of Ireland we were interested in modelling various aspects of the Irish Economy but quarterly national accounts were not available in Ireland until the late 90's. In the early 80's I wrote Chow-Lin routines first in TROLL and then in Gauss and calculated several sets of quarterly national accounts which were used in modelling various aspects of the economy. <br>
<br>Manual.pdf describes a RATS program used to produce a set of national accounts for the Irish macro model component of the ECB systrm of macro-models. The implementation assumes a certain relationship between the annual variable and the quarterly indicators. If the model is valid then the estimates are unbiased. As the model is probably not valid some bias certainly exists. However analysis using the derived data has generally produced reasonable useful results.<br>
<br>The methodology used differs from the original Chow-Lin methodology. Given the assumed model between the unobserved quarterly model and the indicators one can calculate the distribution of the annual data and estimate the parameters using maximum likelihood.<br>
<br>Manual2.pdf describes an extension of the methodology where quarterly data are available for some of the period and the likelhodd estimation is based on the distribution of the quarterly data where available and on the annual observations otherwise. I think that tis method has been used in the Central Bank but I do not know the extent of that use as I retired from the Central Bank about 5 years ago.<br>
<br>The method can be used to decompose a series by using a constant and/or trend as indicators. In most cases of interest there is some indicator.<br><br>In some cases one may set up some form of penalty function to be minimised and assume that the quarterly series follows some form of time series.<br>
<br>A long time ago we also experimented a little with Kalman Filters with limited sucess. These methods might be easier with current computer facilities. <br><br>I <br><br><div class="gmail_quote">2009/10/30 Brian G. Peterson <span dir="ltr"><<a href="mailto:brian@braverock.com">brian@braverock.com</a>></span><br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">John,<br>
<br>
Thanks for the pointers. I'll certainly research this method. <br>
It appears on first reading that the Chow-Lin method requires multivariate indicator series to attempt to remove biases potentially introduced by the higher frequency indicators. Do I understand this correctly? <br>
If so, is the resulting series truly "unbiased", or are the biases introduced by construction via the indicator, and then minimized by the distribution of the residual?<br>
<br>
Also, you would not be able to decompose a univariate series of annual numbers, as I thought was proposed by the original poster in this thread, without identifying indicators. Correct?<br>
<br>
Thanks again,<br><font color="#888888">
<br>
- Brian</font><div><div></div><div class="h5"><br>
<br>
John Frain wrote:<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Several Official Bodies (Central Banks Eurostat etc) use Chow-Lin<br>
interpolation to derive quarterly data from annual or monthly from<br>
quarterly. It may be the case that the higher frequency data have<br>
only recently been produced. I don't know if anyone has produced any<br>
R routines but if you google "Chow-Lin interpolation" you will<br>
probably find implementations in Matlab or Gauss that should transfer<br>
easily to R. My implementation in RATS can be accessed at<br>
<a href="http://ideas.repec.org/p/cbi/wpaper/2-rt-04.html" target="_blank">ideas.repec.org/p/cbi/wpaper/2-rt-04.html</a>. This contains an<br>
explanation of the methodology and references to the original papers.<br>
<br>
The research section of the Eurostat website also contained some<br>
relevant material.<br>
<br>
Best Regards<br>
<br>
John<br>
<br>
2009/10/30 Brian G. Peterson <<a href="mailto:brian@braverock.com" target="_blank">brian@braverock.com</a>>:<br>
<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Axel Leroix wrote:<br>
<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Hi,<br>
This is a newbie question. I would to be able to convert annual time<br>
series of flow data into quarterly data. I wonder if there is any existing<br>
R-function which permits to do it? In what package ?<br>
I the archive, i found that some poeple speak about "tempDis" package for<br>
performing time series temporal disaggregation, but when I try to download<br>
it I can not found it in the list of proposed packages. Thank you in<br>
advance for your help.<br>
<br>
<br>
</blockquote>
Well, as discussed multiple times on this list, going from annual (or any<br>
lower frequency) data to quarterly (or any higher frequency) data is<br>
questionable at best. Think data snooping or look-ahead bias in your<br>
modeling.<br>
<br>
Going the other direction, from say daily (or any higher frequency) to<br>
monthly (or any lower frequency) , is easily accomplished with to.period for<br>
price/value data or Return.cumulative for returns data.<br>
<br>
If you really do want to go in the black-magic direction of going from<br>
annual to quarterly, first make sure that the "annual" data was not first<br>
reported as monthly data or quarterly data (this is true for almost all<br>
macroeconomic series) and then go back to the source data at a higher<br>
frequency.<br>
<br>
If even this is not possible, and you insist on the highly dubious practice<br>
of taking an annual number and turning it into four quarterly numbers, see<br>
the various na handling methods provided by the zoo package, most likely<br>
na.approx or na.spline.<br>
<br>
Regards,<br>
<br>
- Brian<br>
<br>
--<br>
Brian G. Peterson<br>
<a href="http://braverock.com/brian/" target="_blank">http://braverock.com/brian/</a><br>
Ph: 773-459-4973<br>
IM: bgpbraverock<br>
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</blockquote>
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</div></div></blockquote></div><br><br clear="all"><br>-- <br>John C Frain, Ph.D.<br>Trinity College Dublin<br>Dublin 2<br>Ireland<br><a href="http://www.tcd.ie/Economics/staff/frainj/home.htm">www.tcd.ie/Economics/staff/frainj/home.htm</a><br>
mailto:<a href="mailto:frainj@tcd.ie">frainj@tcd.ie</a><br>mailto:<a href="mailto:frainj@gmail.com">frainj@gmail.com</a><br>