cforecast: Conditional Forecasting and Scenario Analysis Using VAR Models
Provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models.
Implements a Kalman smoothing 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>.
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
BVAR, dplyr, FKF, KFAS, miscTools, tibble, vars, utils, methods, wex |
| Published: |
2026-06-08 |
| DOI: |
10.32614/CRAN.package.cforecast |
| Author: |
Tim Ginker  [aut,
cre] |
| Maintainer: |
Tim Ginker <tim.ginker at gmail.com> |
| BugReports: |
https://github.com/timginker/cforecast/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/timginker/cforecast |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
cforecast results |
Documentation:
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