Dear all,   I have two models for daily ranges prediction, one of them being a pure VECM of high and low prices and the other one being the same augmented by other variables. On in sample, the first model has RSQ~20% for both variables, the second has RSQ~55% both both. However, when I do rolling forecasts, the forecasts end up being nearly the same, DMW test says they are of the same quality. And this is surprising for everybody I told. I use the following script for rolling window forecasting. Am I doing everything all right? The OOS forecasts are different by manual comparison, but their statistical similarity is striking.   Thank you, Daniel   # We create one-step-ahead forecasts of models estimated on a rolling window of 400 observations #OOSMatrix contains predictions of daily ranges, i.e. dailyHigh - dailyLow #open D:\Thesis\Rolling_Forecasts\total_data.gdt smpl full scalar DataLen = $nobs scalar WindowLen = 400 scalar FirstObs = 1 scalar LastObs = WindowLen #Out of Sample Forecasting matrix  OOSMatrix = zeros( DataLen - WindowLen, 1) loop ForecastPeriod = LastObs + 1..DataLen - 1  --quiet     smpl FirstObs LastObs         # set data range     # VECM pure with 6 lags     #vecm 7 1 AllHigh AllLow --quiet     # VECM with 6 lags and changes in O, C and daily returns     vecm 7 1 AllHigh AllLow; d_AllOpen d_AllOpen(-1) d_AllOpen(-2) d_AllOpen(-3) d_AllOpen(-4) d_AllOpen(-5)  d_AllClose(-1) d_AllClose(-2) ret(-1)     fcast (LastObs+1) (LastObs+1) --quiet     OOSMatrix[ForecastPeriod - WindowLen] = $fcast[1] - $fcast[2]         FirstObs = FirstObs + 1     LastObs = LastObs + 1 endloop