But I would like to understand the differences so that I know which one I need to use. comp(10,t) gdp cpi gdp m1 performs the same variance decomposition as above using a different ordering. You see what I mean? There is probably just a simple thing I'm missing. The second line tabulates the variance decompositions of GDP up to 10 periods using the ordering as specified in VAR1. Forecasting Now you can produce forecasts directly from an estimated VAR object by updating the Forecast dialog box.
For forecast step 20, the FEVD returns the following values: VAR (I): XA: 80 on XA / 20 on YA YA: 10 on XA / 90 on YA.
forecast I get NA in the forecasting variance and covariance and the same out of sample forecasted values. YB (t) a (b2) + b (b2,1)XB (t-1) + b (b2,2)YB (t-1) + e (b2,t) Now we conduct forecast error variance decomposition on the two VAR-models. I figured the growth numbers would just be added up to get the actual values. EViews offers an entirely new interface for mixing graph types, allowing greater flexibility in choosing combinations of graph types to show on the same graph. Moderators: EViews Gareth, EViews Moderator. Or it's hte same as the forecasted d(y) values suggest, but then I don't understand I ask to give the output as y, why there are different results b/w dynamic and statistic. (not considering diffrences to the lenght of the forecast, just the figures for the in_sample forecast)īecause I thought either the values are treated differently by Eviews (regarding the discussion we had earlier), but then the d(y) should also give different results. No, I actually understand that part I mean that the first thing you read and not too hard to understand.Īnd from your answers I get that the d(y) is also treated as dynamic value, since it's been calculated using y(-1), right?īut now I don't see why I get the same results forecasting d(y) and different results for forecasting y.