Getting Started Example for PROC AUTOREG
/*--------------------------------------------------------------
SAS Sample Library
Name: autgs.sas
Description: Example program from SAS/ETS User's Guide,
The AUTOREG Procedure
Title: Getting Started Example for PROC AUTOREG
Product: SAS/ETS Software
Keys: autoregression
PROC: AUTOREG
Notes:
--------------------------------------------------------------*/
/* Regression with Autocorrelated Errors */
data a;
ul = 0; ull = 0;
do time = -10 to 36;
u = + 1.3 * ul - .5 * ull + 2*rannor(12346);
y = 10 + .5 * time + u;
if time > 0 then output;
ull = ul; ul = u;
end;
run;
title 'Autocorrelated Time Series';
proc sgplot data=a noautolegend;
series x=time y=y / markers;
reg x=time y=y/ lineattrs=(color=black);
run;
proc autoreg data=a;
model y = time;
run;
ods graphics on;
proc autoreg data=a;
model y = time / nlag=2 method=ml;
run;
proc autoreg data=a;
model y = time / nlag=2 method=ml;
output out=p p=yhat pm=trendhat;
run;
title 'Predictions for Autocorrelation Model';
proc sgplot data=p;
scatter x=time y=y / markerattrs=(color=blue);
series x=time y=yhat / lineattrs=(color=blue);
series x=time y=trendhat / lineattrs=(color=black);
run;
data b;
y = .;
do time = 37 to 46; output; end;
run;
data b;
merge a b;
by time;
run;
proc autoreg data=b;
model y = time / nlag=2 method=ml;
output out=p p=yhat pm=ytrend
lcl=lcl ucl=ucl;
run;
title 'Forecasting Autocorrelated Time Series';
proc sgplot data=p;
band x=time upper=ucl lower=lcl;
scatter x=time y=y;
series x=time y=yhat;
series x=time y=ytrend / lineattrs=(color=black);
run;
/*-- Durbin-Watson test for autocorrelation --*/
proc autoreg data=a;
model y = time / dw=4 dwprob;
run;
data b;
set a;
ylag = lag1( y );
run;
proc autoreg data=b;
model y = ylag / lagdep=ylag;
run;
/*-- stepwise autoregression --*/
proc autoreg data=a;
model y = time / method=ml nlag=5 backstep;
run;
data a;
do time = -10 to 120;
s = 1 + (time >= 60 & time < 90);
u = s*rannor(12346);
y = 10 + .5 * time + u;
if time > 0 then output;
end;
run;
title 'Heteroscedastic Time Series';
proc sgplot data=a noautolegend;
series x=time y=y / markers;
reg x=time y=y / lineattrs=(color=black);
run;
/*-- test for heteroscedastic OLS residuals --*/
proc autoreg data=a;
model y = time / archtest;
output out=r r=yresid;
run;
/*-- data with outliers at observation 10 --*/
data b;
do time = -10 to 120;
s = 1 + (time >= 60 & time < 90);
u = s*rannor(12346);
y = 10 + .5 * time + u;
if time = 10 then
do; y = 200; end;
if time > 0 then output;
end;
run;
/*-- test for heteroscedastic OLS residuals --*/
proc autoreg data=b;
model y = time / archtest=(qlm) ;
model y = time / archtest=(lk,wl) ;
run;
data c;
ul=0; ull=0;
do time = -10 to 120;
s = 1 + (time >= 60 & time < 90);
u = + 1.3 * ul - .5 * ull + s*rannor(12346);
y = 10 + .5 * time + u;
if time > 0 then output;
ull = ul; ul = u;
end;
run;
title 'AR(2)-GARCH(1,1) model for the Y series regressed on TIME';
proc autoreg data=c;
model y = time / nlag=2 garch=(q=1,p=1) maxit=50;
output out=out cev=vhat;
run;
data out;
set out;
shat = sqrt( vhat );
run;
title 'Predicted and Actual Standard Deviations';
proc sgplot data=out noautolegend;
scatter x=time y=s;
series x=time y=shat/ lineattrs=(color=black);
run;
/*-- AR(2)-EGARCH(1,1) model --*/
proc autoreg data=a;
model y = time / nlag=2 garch=(p=1,q=1,type=exp);
run;
/*-- test for stationarity of regression residuals --*/
proc autoreg data=a;
model y= / stationarity = (KPSS);
run;
/*-- test for stationarity using quadratic
spectral kernel and automatic bandwidth selection --*/
proc autoreg data=a;
model y= /
stationarity = (KPSS=(KERNEL=QS AUTO));
run;