Proc ARIMA fitting problem

[niam]

I have a data set of time series observations with a seasonal trend.

I have de-trended and de-seasonalized it using by first differencing and also taking the log of observations. However, when I want to fit an ARIMA model, It is almost impossible to have a proper fit.

This is my code and I have attached the data set.

proc import datafile="liquor.csv"

out=liquor

dbms=csv

replace;

getnames=no;

run;

data liquor (rename=(var1=sales));

set liquor;

t = _n_ ;

*The log transformation is predeferencing transformation to make constant seasonal variation;

LogSales=log(sales);

*These are three different differencing transformation to make the data stationary;

SALES1=logsales-lag(logsales);

SALES2=logsales-lag12(logsales);

SALES3=logsales-lag(logsales)-lag12(logsales)-lag13(logsales);

x=sales1-lag12(sales1);

run;

proc timeseries data=liquor

                   out=series

                   outtrend=trend

                   outseason=season print=seasons;

      id date interval=month accumulate=avg;

      var x;

   run;

proc arima data=LIQUOR ;

      identify var=x esacf ;

      estimate  p=2 q=3;

   run;

Any suggestions and help is greatly appreciated.

[niam · Accepted solution]

I am closing this discussion due to inactivity.