[R] Partitioning an nxp Time series matrix

[Paul Gilbert]

Neil 

There is some related work by Stock and Watson, Forni and Reichlin, and
Quah and Sargent (see below) which tries to extact "dynamic factors"
from large cross sections of time series. As far as I know, no one has
gone the second step of clustering the data, but their work might be a
good starting point. (Sounds like a pretty ambitious term project paper
though.)

As far as I know, no one has implemented any of this in R (but I would
be happy to hear otherwise). I am working on something that is
(marginally) related.

Paul Gilbert

Forni, M., and L. Reichlin 1996. "Dynamic Common Factors in Large
Cross-Sections" Empirical Economics 21: 27-42.

Quah, D., and T. J. Sargent 1994. "A dynamic index model for large cross
sections." in Business Cycles, Indicators, and Forecasting, J. Stock and
M. Watson (eds). NBER and Univ. Press Chicago.

Stock, J. H. and Watson, M. W. 1999 "Forecasting Inflation," Journal of
Monetary Economics 44: 293-335.


Neil Osborne wrote:
> 
> Hi,
> 
> I have an nxp data set of time series. For my final year project, I would
> like to partition this data set into a smaller number of units ( < n). Where
> each of the units contains time series that move closely together (i.e. in
> unison), and the "simmilarity" (in terms of occurence & amplitudes of peaks
> and troughs) between the segregated units is "low".
> 
> I know these are slightly "fuzzy" terms, but I want to get to the essence of
> what I'm trying to achieve (time series partitioning), without throwing in
> words like correlation or cointegration which are "trigger" words. I would
> like to know if anyone has done this kind of partitioning before ?
> 
> Any tips or suggestions as to how to carry out such a partioning in R will
> be MOST welcome. Ideally, I would like to automate this partitioning through
> an R "script" since n will be approx 500, and it will not be feasible to do
> this manually.
> 
> Any pointers/help will be very much appreciated
> 
> Regards
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