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.Rbuildignore

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^\.Rproj\.user$
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^LICENSE\.md$
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^README\.Rmd$
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^cran-comments\.md$

R/variable_importance_level.R

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#'
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#' # Classical VAR
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#' fit <- VAR(Canada, p = 2, type = "const")
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#' # conditioning on U, target variable is prod, horizon is set to 1 to speed up the computation
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#' # conditioning on U, target variable is prod,
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#' # horizon is set to 1 to speed up the computation
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#' vim <- variable_importance_level(fit = fit, cond_var = 4, target_var = 2, horizon = 1)
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#'
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variable_importance_level <- function(fit, cond_var, target_var, horizon){

R/variable_importance_stat.R

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#' data(fred_macro)
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#' # Fit a VAR model
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#' fit <- VAR(fred_macro[,-1], p = 2, type = "const")
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#' # conditioning on the oil price, target variable is core inflation, horizon is set to 1 to speed up the computation
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#' # conditioning on the oil price, target variable is core inflation,
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#' # horizon is set to 1 to speed up the computation
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#' vim <- variable_importance_stat(fit = fit, cond_var = 5, target_var = 2, horizon = 1)
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#'
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#'

README.Rmd

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The example below replicates an empirical experiment from:
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> Caspi, I., & Ginker, T. (2026). *What Drives the Scenario? Interpreting Conditional Forecasts in Reduced-Form VARs*.
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> https://doi.org/10.13140/RG.2.2.25225.51040
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>
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The illustration demonstrates the workflow for scenario design, conditional forecasting, and forecast attribution.
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README.md

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The example below replicates an empirical experiment from:
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> Caspi, I., & Ginker, T. (2026). *What Drives the Scenario?
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> Interpreting Conditional Forecasts in Reduced-Form VARs*.
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> <https://doi.org/10.13140/RG.2.2.25225.51040>
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> Interpreting Conditional Forecasts in Reduced-Form VARs*.
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The illustration demonstrates the workflow for scenario design,
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conditional forecasting, and forecast attribution.

cran-comments.md

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## R CMD check results
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0 errors | 0 warnings | 0 notes
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* This is a new release.

man/variable_importance_level.Rd

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man/variable_importance_stat.Rd

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