Hello
How to implement dynamic regression model like
(1 - u1*B-u2*B^2)*(1-u24*B^24)*(1-u168*B^168)*Yt = (vo+v1*B+v2*B^2)*Xt + Et
with Et WN?
I have just read the proc arima user guide...And I don't know to transform my model in a transfer function model....
please help me!
Hello
How to implement dynamic regression model like
(1 - u1*B-u2*B^2)*(1-u24*B^24)*(1-u168*B^168)*Yt = (vo+v1*B+v2*B^2)*Xt + Et
with Et WN?
If it is about Time Series Analysis, plz post it at Forecasting Forum. Proc arima can do dynamic regression.
Ok..I post there my msg.
But I have just read the proc arima user's guide.....It declare to write in a transfer function form..In my case is not ok!
you could try the following estimate statement in proc arima:
estimate p=((1,2)(24)(168)) input((1,2)x);
thanks for the reply
I think, in this case, to write Yt = (v0 + v1*B + v2*B^2)*Xt + 1/((1-u1*B - u2*B^2)*(1-u24*B^24)*(1-u168*B^168))*Et
right or not?
i think you need to divide the backshift operator for Xt as well..
Yt = (v0 + v1*B + v2*B^2)/((1-u1*B - u2*B^2)*(1-u24*B^24)*(1-u168*B^168))*Xt + 1/((1-u1*B - u2*B^2)*(1-u24*B^24)*(1-u168*B^168))*Et
Anyone have others idea?
Sorry, what do you mean by "impose the two denominator to be equal?"? One more thing, you need to add an NOINT to the ESTIMATE statement as there is no intercept term in ur model.
Thank for the reply:
If I write my ARX like this
Yt = (v0 + v1*B + v2*B^2)/((1-u1*B - u2*B^2)*(1-u24*B^24)*(1-u168*B^168))*Xt + 1/((1-u1*B - u2*B^2)*(1-u24*B^24)*(1-u168*B^168))*Et
(whit NOINT option) the parameters of the two denominator are estimated different:
Yt = (v0 + v1*B + v2*B^2)/((1-u1a*B - u2b*B^2)*(1-u24a*B^24)*(1-u168a*B^168))*Xt + 1/((1-u1b*B - u2b*B^2)*(1-u24b*B^24)*(1-u168b*B^168))*Et
whit uia != uib
but in my model uia ==uib
You can specify Y = (numerator poly) / (denominator poly) X + E / (AR Poly) type model for your polynomials as follows:
proc arima data=test;
identify var=y crosscorr=x;
estimate p=(1 2)(24)(168)
input=((1 2)/(1 2)(24)(168) x)
noint;
run;
It will indeed happen that the estimated denominator polynomial coefficients in the transfer function will generally be different from the estimated AR polynomial coefficients. Currently PROC ARIMA does not allow constraining these coefficients to be the same.
You seem to be dealing with hourly data and are trying to capture hour of the day and hour of the week seasonal patterns. Is this particular model very important for you or some other model might do that still does a good job?
thank for the reply rselukar
you have grasped fully my problem. It is a model of electricity price (Y) related to demand (X). I need to re-execute examples of a book for my thesis. I am searching others model too.
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