cforecast

cforecast is an R package for conducting conditional forecasts and scenario analysis using vector autoregressive (VAR) models. It implements the Kalman filtering methodology proposed by Clarida and Coyle (1984) and Banbura, Giannone, and Lenza (2015), allowing users to simulate forecast paths under imposed constraints on future values of selected variables.

Installation

You can install the development version from GitHub using:

# install.packages("devtools")
devtools::install_github("timginker/cforecast")

Example

library(cforecast)
library(vars)
library(ggplot2)

# Load data and fit VAR model
data(Canada)
fit <- VAR(Canada, p = 2, type = "const")

# Define a scenario: increase unemployment (4th variable) to 15 for 3 periods
cond_path <- matrix(rep(15, 3), ncol = 1)
cond_var <- 4

# Generate conditional forecast
conditional_forecast <- cforecast(fit = fit, cond_path = cond_path, cond_var = cond_var)

# Extract forecast and standard deviation
forecast <- conditional_forecast$forecast[, 1]
std_dev <- sqrt(conditional_forecast$mse[1, 1, ])
horizon <- length(forecast)
time <- seq_len(horizon)

df <- data.frame(
  Time = time,
  Forecast = forecast,
  Lower = forecast - 1.645 * std_dev,
  Upper = forecast + 1.645 * std_dev,
  Series = "Forecast"
)

# Plot
ggplot(df, aes(x = Time, y = Forecast)) +
  geom_ribbon(aes(ymin = Lower, ymax = Upper), fill = "grey70", alpha = 0.4) +
  geom_line(color = "black", linewidth = 0.6) +
  labs(
    x = "Horizon",
    y = "Forecasted Value",
    title = "Conditional Forecast with 90% Confidence Interval"
  ) +
  theme_minimal(base_size = 12, base_family = "serif") +
  theme(
    legend.position = "none",
    axis.title = element_text(face = "bold"),
    plot.title = element_text(hjust = 0.5, face = "bold"),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()
  )

References

• Clarida, R., & Coyle, D. (1984). Conditional Projection by Means of Kalman Filtering.
• Banbura, M., Giannone, D., & Lenza, M. (2015). Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections. International Journal of Forecasting, 31(3), 739–756.

Disclaimer

The views expressed here are solely of the author and do not necessarily represent the views of the Bank of Israel.

Please note that cforecast is still under development and may contain bugs or other issues that have not yet been resolved. While we have made every effort to ensure that the package is functional and reliable, we cannot guarantee its performance in all situations.

We strongly advise that you regularly check for updates and install any new versions that become available, as these may contain important bug fixes and other improvements. By using this package, you acknowledge and accept that it is provided on an “as is” basis, and that we make no warranties or representations regarding its suitability for your specific needs or purposes.