On Thu, 7 Jan 2016, Marc O'Callaghan wrote:
> I don't remember exactly, offhand, what the project was here,
but it
> sounnds like you need to look at the $coeff accessor (to grab the exact
> coefficients after estimation) and the lincomb() function (to create a
> series via a linear combination of terms).
>
> Allin Cottrell
Thanks for your answer, though I think I didn't express myself correctly.
For the context : I am studying the evolution of max and min temperatures
throught Feb-March-Apr of every year, with special emphasis on March. My main
goal is to sort them into two categories : 1) when they have a tendency to
rise during the month and 2) when they tend to sink.
So as to avoid plotting a graph for each year and judging on the basis of
that what the tendency is, I was planning to plot the 3rd-degree regression
using the "polynomial trend" (or whatever it's called in English, I'
sorry, I
downloaded Gretl in French to better understand what the others in my group
talk about) filter. Can I somehow find out what that polynom is ? Does this
seem like an intelligent way to go ?
If not, could you please be so good as to explain how you would recommend I
proceed, keeping in mind that I am (to my great shame) an utter nincompoop in
matters Gretl ?
If your dataset looks something like what I recommended in
http://lists.wfu.edu/pipermail/gretl-users/2015-December/011431.html
where "yrday" is a series that runs from 1 to 90 for the 90 days
starting on Feb 1 each year and TMAX records maximum temperature,
then you could run a regression as follows
series yd2 = yrday^2
series yd3 = yrday^3
ols TMAX const yrday yd2 yd3
That will give you your 3rd degree polynomial trend for TMAX.
Allin Cottrell