Hi, I would like to know how to add time fixed effect as well as newey west t stat together when dealing with time series data regression. To be specified, I now have a 10-year daily data which is autocorrelated as y variable, and several time series x variables. I want to add year dummies (as fixed effects) and also use newey west t stat (or other t stat that adjusts for autocorrelation). I know "proc model " can deal with newey west t stat, but it cannot add time fixed effects neatly as "proc glm" by using "class" or "absorb" statement, and now I can only add year dummies mannually, which is not feasible when dealing with hundreds or thousands levels of fixed effect. I wonder: (1) is there any way to add year fixed effect dummies conviniently using proc model? (2) if not, is there any other statement rather than "proc model" that support a autocorrelated adjusted t stat (eg. newey west t stat) as well as neat time fixed effect? Thanks! ... View more

     I want to estimate a GARCH-GJR model and get the estimated parameters and their variance-covariance matrix.       The main estimation part of the code is presentes below. I just specified a standard GJR model and estimated by "proc model" procedure, and get the varcov matrix in out1.       However, although the model is succefully converged and there is not any warnings or errors in log (as posted), the variance-covvariance matrix in out1 has negative variance element. As show in the dateset screenshot, for the data of FX_Australia in 1970s, the variance of a1 is -6.648735E-6, which is negative.  And there is not any warnings or errors in the log.       I wonder what it means to have a non-positive definite var-cov matrix and how to deal with it?        Thanks!       %let ret=ret; %let lv=long_run_var; proc model data = local_inds noprint; parms mu 0 a0 .05 a1 .04 g1 .9 a2 0.1; /* mean model */ &ret. = mu ; /* variance model */ if zlag(&ret.-mu) > 0 then h.&ret. = a0 + a1*xlag((&ret.-mu)**2,mse.&ret.) + g1*xlag(h.&ret.,mse.&ret.); else h.&ret. = a0 + a1*xlag((&ret.-mu)**2,mse.&ret.) + g1*xlag(h.&ret.,mse.&ret.) + a2*xlag((&ret.-mu)**2,mse.&ret.); /* fit the model */ /* fit &ret. start=(mu=0 a0=0.05 a1=0.04 g1=0.9 a2=0.1 -0.1) / startiter method = marquardt fiml outest=out outcov MAXITER=1000;*/ fit &ret. / method = marquardt fiml outest=out1 outcov MAXITER=1000; restrict (a0/(1-a1-g1-0.5*a2))=%SYSEVALF(&lv.); restrict a0>0; restrict g1>0; by decade; run ; quit ;   ... View more