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05-01-2017 09:58 AM - edited 05-03-2017 04:20 PM

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 ?

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05-02-2017 09:02 AM

ACF is decreasing very slowly, therefore price could not be stationarity.

Difference it .

`identify var=price(1)`

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05-02-2017 12:03 PM - edited 05-03-2017 04:20 PM

i see that's the same like:

newprice=price-lag1(price);

It is stationary now, but some prices are negative.

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05-03-2017 08:08 AM

Use SAS syntax (forecast statement), and SAS will take care of it. identify var=price(1); estimate p=2; forecast lead=12

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05-03-2017 08:17 AM

If you want constraint price be positive , Try PROC ESM . Or "Forecasting Log Transformed Data" , check it in PROC ARIMA's documentation.

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05-03-2017 08:58 AM

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

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05-03-2017 09:23 AM

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 .