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cforecast  

Conditional Forecasting and Scenario Analysis Using VAR Models
View on CRAN: Click here


Download and install cforecast package within the R console
Install from CRAN:
install.packages("cforecast")

Install from Github:
library("remotes")
install_github("cran/cforecast")

Install by package version:
library("remotes")
install_version("cforecast", "0.1.0")



Attach the package and use:
library("cforecast")
Maintained by
Tim Ginker
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-09
Latest Update: 2026-03-09
Description:
Provides tools for conducting scenario analysis in reduced-form vector autoregressive (VAR) models. Implements a Kalman filtering framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) <doi:10.13140/RG.2.2.25225.51040>.
How to cite:
Tim Ginker (2026). cforecast: Conditional Forecasting and Scenario Analysis Using VAR Models. R package version 0.1.0, https://cran.r-project.org/web/packages/cforecast. Accessed 12 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:25), 0.1.0 (2026-03-09 12:00)
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Complete documentation for cforecast
Functions, R codes and Examples using the cforecast R package
Full cforecast package functions and examples
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