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>.
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forecastingscenario-analysistime-seriestime-series-analysistime-series-forecastingtime-series-predictionvector-autoregression
4.66 score 13 stars 530 downloadswex - Exact Observation Weights for the Kalman Filter and Smoother
Computes exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>. The package provides tools for analyzing linear Gaussian state-space models, allowing users to quantify the contribution of individual observations to filtered and smoothed state estimates. These weights can be used for interpretation, decomposition, and diagnostic analysis in time series models, including applications such as dynamic factor models. See the README for examples.
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kalman-filterkalman-filteringtime-series-analysis
4.13 score 3 stars 1 dependents 1 scripts 572 downloadsboiwsa - Seasonal Adjustment of Weekly Data
Perform seasonal adjustment and forecasting of weekly data. The package provides a user-friendly interface for computing seasonally adjusted estimates and forecasts of weekly time series and includes functions for the construction of country-specific prior adjustment variables, as well as diagnostic tools to assess the quality of the adjustments. The methodology is described in more detail in Ginker (2024) <doi:10.13140/RG.2.2.12221.44000>.
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seasonal-adjustmentseasonalitytime-series-analysis
3.48 score 6 stars 10 scripts 276 downloads