data arima.js_dispos;
set arima.js_2005to2018;
label year="y" cases="c";
run;
proc gplot data=arima.js_dispos;
plot cases*year;
symbol i=spline v=star ci=red cv=bib;
run;
proc arima data=arima.js_dispos;
identify var=cases stationarity=(adf=1);
run;
data arima.js_dispos_dif2;
set arima.js_dispos;
z=cases;
difz=dif(z);
difz2=dif(difz);
label z="n" difz="d1" difz2="d2" ;
run;
proc gplot data=arima.js_dispos_dif2;
plot (z difz difz2)*year;
symbol i=spline v=star ci=red cv=bib;
run;
proc arima data=arima.js_dispos_dif2;
identify var=difz2 stationarity=(adf=1);
run;
proc arima data=arima.js_dispos_dif2;
identify var=difz2 stationarity=(adf=1) WHITENOISE=IGNOREMISS;
run;
proc arima data=arima.js_dispos_dif2;
identify var=difz2 minic perror=(8:11);
run;
/*test proc arima data=arima.js_dispos_dif2;
identify var=difz2 minic P=(0:5) Q=(0:5) ;
run;
---------------test end*/
proc arima data=arima.js_dispos_dif2;
identify var=z(2) nlag=6;
estimate q=0 p=0 method=ml;
forecast lead=5 out=arima.outvalue id=year;
run;
quit;
