I have a time series with prices. And i need to check if it stationary or not ?And if not, i need to make it stationary.
So i think, i have to do a Dickey Fuller test.
So i tried this code
proc ARIMA data=prices;
identify var=price stationarity=(adf=(0)) ;
run;
Is it correct ?
Sorry . mislead you.
newprice=price-lag1(price);
are supposed to get negative value like -0.0568, -0.2654.
if you put model on newprice ,you will get difference of price 's forecast,
therefore suggest you to use PROC ARIMA syntax, not calculated it by hand .
ACF is decreasing very slowly, therefore price could not be stationarity.
Difference it .
identify var=price(1)
i see that's the same like:
newprice=price-lag1(price);
It is stationary now, but some prices are negative.
Use SAS syntax (forecast statement), and SAS will take care of it. identify var=price(1); estimate p=2; forecast lead=12
If you want constraint price be positive , Try PROC ESM . Or "Forecasting Log Transformed Data" , check it in PROC ARIMA's documentation.
I have very small variables. It's gas prices.
So after difference i get for example some prices like -0.0568, -0.2654
I tried this
data new;
set newprices;
ylog = log( newprices );
run;
But after this i get :
-2.356,-2.0325 etc.
i don't understand
Sorry . mislead you.
newprice=price-lag1(price);
are supposed to get negative value like -0.0568, -0.2654.
if you put model on newprice ,you will get difference of price 's forecast,
therefore suggest you to use PROC ARIMA syntax, not calculated it by hand .
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