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Posted 04-15-2018 02:13 PM
(2036 views)

I am having difficulty coming up with the transfer model inputs. Below is the code that I have so far

proc arima data = sales plot(unpack); identify var = x noprint; estimate q = 2 noprint; identify var = y crosscorr=(x) nlag = 20; /*estimate input = (3$(2)/(1,1)x) noint printall altparm backlim = -3 plot;*/ /*estimate input = (3$(2)/(2)x) noint printall altparm backlim = -3 plot;*/ estimate input = (3$(1)/(1,2)x) noint printall altparm backlim = -3 plot; run; quit;

My question is how to come up with numerator and denominators based on the graph and differencing. I know b = 3$ since it is the number of periods it takes before xt affects yt

__For s and r , I am confused:__

s is from my understanding the number of lags that reside between the first spike and the beginning of the clear dying down pattern

since I have x and y with no differencing and I set s arbitrarily to s = 2 would I write input = 3$(1,2)/(1,2)

r is from my understanding 1 if the lags die down exponentially after the spikes or 2 if they down in a sine wave

since I have x and y with no differencing and since from the graph r = 2 would I input in SAS input = 3$(1,2)/(1,2)

I am confused on what specifically (1,2) means: does it mean first order differencing and r/s being chose at 2 or does 1,2 mean how many lags before there is a spike

If anyone can help me interpret my graph I would greatly appreciate it .

Thank you

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Deciding the form of a transfer function relationship is explained in a book by Pankratz, A. (1991). *Forecasting with Dynamic Regression Models*. New York: John Wiley & Sons. Also see Pankratz, A. (1983). *Forecasting with Univariate Box-Jenkins Models: Concepts and Cases*. New York: John Wiley & Sons. You can also check out professor Hyndman's blog on time series analysis: https://robjhyndman.com/hyndsight/arimax/

Hope this helps.

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Deciding the form of a transfer function relationship is explained in a book by Pankratz, A. (1991). *Forecasting with Dynamic Regression Models*. New York: John Wiley & Sons. Also see Pankratz, A. (1983). *Forecasting with Univariate Box-Jenkins Models: Concepts and Cases*. New York: John Wiley & Sons. You can also check out professor Hyndman's blog on time series analysis: https://robjhyndman.com/hyndsight/arimax/

Hope this helps.

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