I am working with a time-series dataset that is based on demand-supply dynamics with variables: Production, Ending Stocks, Exports, Imports etc. I was working on a regression with this data and wanted to find the relative importance of regressors using relaimpo package in R. However, I soon realized I can't use regression setting because of near to perfect collinearity in my dataset - the standard errors will be inflated and hence will be the relative importance of each regressor. Is there a way to go around this? Or, can I use any other method (instead of regression)? Any suggestions will be useful.
I am working with a time-series dataset that is based on demand-supply dynamics with several variables.  THe sample data for one time period is: &gt; data # A tibble: 19 x 6    Pred_error_A Pred_error_B  Pred_error_C  Pred_error_D  Pred_error_E  Pred_error_Target                  &lt;dbl&gt;         &lt;dbl&gt;          &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;           &lt;dbl&gt;  1            -0.45           1.72          -0.170      1.85       -0.01          -0.7    2            -0.45           1.72          -0.170      2.35       -0.01          -1.2    3            -0.45           1.92          -0.170      2.35       -0.01          -1      4            -0.0500         0.37          -0.12       1.65       -0.01          -1.5    5             0             -0.840         -0.12       0.950      -0.01          -1.7    6             0             -0.200         -0.12       1.35       -0.01          -1.5    7             0             -0.460         -0.12       1.35       -0.01          -1.8    8             0             -0.520         -0.12       1.05       -0.01          -1.5    9             0             -0.340         -0.12       1.05       -0.01          -1.4   10             0             -0.340         -0.12       1.35       -0.01          -1.7   11             0             -0.110         -0.12       1.05       -0.01          -1.2   12             0             -0.110         -0.12       0.850      -0.01          -1     13             0              0             -0.12       0.350      -0.01          -0.4   14             0              0             -0.12      -0.15       -0.01           0.100 15             0              0             -0.12      -0.350      -0.01           0.300 16             0              0             -0.02       0          -0.01          -0.1   17             0              0              0          0           0              0     18             0              0              0          0           0              0     19             0              0              0          0           0              0   I was working on a regression for each time period with data and wanted to find the relative importance of regressors in explaining the target variable ( Pred_error_A through E and dependent variable is Pred_error_Target ) using relaimpo package in R. However, I soon realized I can't use regression setting because of near to perfect collinearity and hence no relative importance values. Is there a way to go around this? Or, can I use any other method (instead of regression)? Any suggestions will be useful. The predictors are near to perfect and each one is important. I can't exclude any single one and I need the relative importance of all 5 regressors. I am also trying XGBoost as an alternative. However, I am not sure if it takes care of multicollinearity.
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