In the default setting, the MAPE was the same. I run the two equations deperately and compared the MAPE (or the RMSE) with those calculated by EViews if the two equations are run together in a model. if one variabe depends on the estimate of another one Eviews gives the value of this dependent one as if the variable it depends one were actuals (i.e. I experimented again because I think it is really an important issue and it should be clear to everyone how EViews works at this point:ĮViews does not - in its default setting - show the true dependence of variables in a model, i.e. Could it be that it is a special behaviour due to the lead in that variable? But I am a little confused and would like to know for sure how it works. I mean, I am fine if EViews works like this (albeight I would say I would expect the default settings being different). So, for me it looks like as if the default setting in EViews is that in case of such dependencies the model is solved by taking the actual (observed) values rather than the estimated ones. Second, in in-sample tests the estimated values from the second (dependent) variable followed relatively closely the pattern of the observed values rather than those of the estimated values of that endogeneous variable - no matter which estimated equation I used (different specifications give different patterns). My logigc is that the error from the frist enters into the second equation and for this reason the error must be at least as large. My suspicion started when I looked at the MAPE which was usually smaller for the variable which depended also on the estimated endogeneous variable from the other equation than that for this variable. (Later, when we describe estimation of restricted VAR models, we relax the identical regressors assumption so that OLS is no longer efficient. I thought so too and was very surprised and thus it took me a while to figure out how the program works. Accordingly, estimation of the standard VAR model in EViews is performed using simple OLS applied to each equation. My question is: Am I right? Is there a simpler way of doing this? Then, if you ant to keep the estimated values from the first equation you have to set the "Solve" => "Solver" to "Preffered solution starting values" to "Previous period's solution". (treat endogeneous variabels as exogeous)". If the true interdependence is to be shown only, if some settings are change (and this is not really convenient):ġ) You have to run the model in its default setting (in order to get the estimates of the endogeneous variable)Ģ) You have to include the respective endogeneous variables into a field in the model menue "Scenarios" => "excludes for. "Specifying scenarios") is at leat misleading and not very clear. if one wants to calculate the etsimation errors (bacause in effect the error of the first equation does not enter into the second one ). The default settig of models is set such in cases with more than one equation (or a system) where endogeneous variables fom one equation enters as an explanatory variable in anoher one, the model solves theses equations but enters not the estimated values solving the other equation but uses the observed ("actual") valuse.
SVAR MODEL IN EVIEWS MANUAL
I find the calculation in a model in default setting at least misleading (the error of the first equation does not appear automatically in the second one).Īlso, the manual is for my taste not very clear on how EViews treats these variables. some endogeneous variables appear with a lead: Please let me know if any more information is required.Working on a system with rational expectations (i.e. I've been stuck on this issue for a few weeks and my supervisor is unavailable. Any guidance with respect to this will be greatly appreciated.
SVAR MODEL IN EVIEWS HOW TO
The paper estimates a few of the SVAR equations using 2SLS and I honestly have no idea how to implement this in a SVAR framework through software packages such as Stata and eviews.I've tried to estimate the short run shocks by themselves but my impulse response functions look really wrong. I'm not entirely sure I understand the paper's restrictions on both the short and long-run matrices of the SVAR.Is there some way of doing this in Stata? If not, what would my alternatives be?