[R-SIG-Finance] Time series temporal disaggregation (or: going from low frequency to higher frequency)

[Brian G. Peterson]

John,

Thanks for the pointers.  I'll certainly research this method. 

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? 

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?

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?

Thanks again,

  - Brian

John Frain wrote:
> Several Official Bodies (Central Banks Eurostat etc) use Chow-Lin
> interpolation to derive  quarterly data from annual or monthly from
> quarterly.  It may be the case that the higher frequency data have
> only recently been produced.  I don't know if anyone has produced any
> R routines but if you google "Chow-Lin interpolation" you will
> probably find implementations in Matlab or Gauss that should transfer
> easily to R.  My implementation in RATS can  be accessed at
> ideas.repec.org/p/cbi/wpaper/2-rt-04.html.  This contains an
> explanation of the methodology and references to the original papers.
>
> The research section of the Eurostat website also contained some
> relevant material.
>
> Best Regards
>
> John
>
> 2009/10/30 Brian G. Peterson <brian at braverock.com>:
>   
>> Axel Leroix wrote:
>>     
>>> Hi,
>>> This is a newbie question. I would to be able to convert annual time
>>> series of flow data into quarterly data. I wonder if there is any existing
>>> R-function which permits to do it? In what package ?
>>>  I the archive, i found that some poeple speak about "tempDis" package for
>>> performing time series temporal disaggregation, but when I try to download
>>> it I can not found it in the list of proposed packages.  Thank you in
>>> advance for your help.
>>>
>>>       
>> Well, as discussed multiple times on this list, going from annual (or any
>> lower frequency) data to quarterly (or any higher frequency) data is
>> questionable at best.  Think data snooping or look-ahead bias in your
>> modeling.
>>
>> Going the other direction, from say daily (or any higher frequency)  to
>> monthly (or any lower frequency) , is easily accomplished with to.period for
>> price/value data or Return.cumulative for returns data.
>>
>> If you really do want to go in the black-magic direction of going from
>> annual to quarterly, first make sure that the "annual" data was not first
>> reported as monthly data or quarterly data (this is true for almost all
>> macroeconomic series) and then go back to the source data at a higher
>> frequency.
>>
>> If even this is not possible, and you insist on the highly dubious practice
>> of taking an annual number and turning it into four quarterly numbers, see
>> the various na handling methods provided by the zoo package, most likely
>> na.approx or na.spline.
>>
>> Regards,
>>
>>    - Brian
>>
>> --
>> Brian G. Peterson
>> http://braverock.com/brian/
>> Ph: 773-459-4973
>> IM: bgpbraverock
>>
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