======================= Question So what about my last question? Are IRF confidence intervals based on bootstrapping valid even if the VECM has non-normal residuals? Answer The bootstrap was developed to avoid Monte Carlo simulations using random draws from a preset distribution (say Normal). The bootstrap will proxy the empirical distribution of your residuals given that the residuals are "sufficiently" noisy (it doesn't matter if they test non-normal!) By noisy, I mean no heteroscedasticity or autocorrelation issues. There is a huge literature for heteroscedasticity issues (Wild bootstrap) and I guess (i am not an expert) block bootstrap that divides your data into blocks to preserve the original time series properties within a block (when dependence is an issue). Back to your question, it seems it has to do with what you mean "non-normal" residuals? My guess is a few outliers or asymmetry? So bootstrap is valid. ======================= Question d.o.f correction Regarding d.o.f corrections when calculating IRFs in gretl Using Allin's example open data9-7 var 4 UNEMP PRIME For the UNEMP equation the Sum squared resid=3.613493 so that sqrt( 3.613493/60)=0.24540759 Notice that this is the one standard error shock in UNEMP and 60 is the original sample size of T=64 obs minus p=4 for the lags (say T-p). In other commercial software this is baptized : no df correction. If you prefer df correction multiply IRFs with 60/(60-1-8) where 1+8 corresponds to the parameters number in one VAR equation sqrt( 3.613493/(60-9))=0.2661819 Now I think the SVAR addon uses the correction sqrt(3.613493/(64-1-8))=0.25631986 that is the original sample size T minus the number of parameters in the UNEMP equation ========================== _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users