[Numpy-discussion] Time series: lag function
v-nijs at kellogg.northwestern.edu
Wed Dec 27 19:01:55 CST 2006
I simplified the function to create lags only along axis 0 (see attached). I
am using c_ now which seems to play nice with 1- and 2-d array's.
The reason I am using 'n = ravel(n)' in the code is that I want to be able
to pass integers as well as lists. For example, I want each of the following
I'd actually also like the following to work:
I could do that with *n but then I don't think I can use range(x,y) in the
same function call. For example, lag(a,1,3,range(6,9)).
You probably don't need this flexibility when calling diff() since, at least
in TS applications, I only ever need diff(a,1) or diff(a,2).
On 12/27/06 6:17 PM, "Sven Schreiber" <svetosch at gmx.net> wrote:
> Vincent Nijs schrieb:
>> I am tryin to convert some of my time-series code written in Ox to
>> scipy/numpy (e.g., unit root tests, IRFs, cointegration, etc). Two key
>> functions I need for this are 'lag' and 'diff'. 'diff' is available but
>> 'lag' is apparently not.
>> Below is my attempt at a lag function. I tried to be somewhat consistent
>> with the diff function which is part of numpy (also listed for convenience).
>> It seems to work fine for a 2-d array but not for a 1-d or 3-d array (see
>> tests at bottom of email). I'd appreciate any suggestions you may have.
> Great to see somebody converting from Ox to numpy, I see synergies ahead!
>> def lag(a, n=1, lag_axis=0, concat_axis=1):
>> Calculate the nth order discrete lag along given axis.
>> Note: axis=-1 means 'last dimension'. This is the default
>> for the diff function. However, the first dimension (0)
>> may be preferred for time-series analysis.
>> a = asanyarray(a)
>> n = ravel(n) # convert input to an array
> why don't you leave n as an integer? maybe you're trying to be too
> clever here. I think it's a good idea to have lag resemble the existing
> diff function, and then a single number n should be enough.
> (And I'm not sure about your concat_axis, e.g. what does axis=1 mean for
> a 1-d array?)
> Do you get your errors also for integer n?
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> Numpy-discussion at scipy.org
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