lmForc (version 1.0.0)

conditional_forc: Linear model forecast conditioned on an input forecast

Description

conditional_forc takes a linear model call, a vector of time data associated with the linear model, and a forecast for each covariate in the linear model. The linear model is estimated once over the entire sample period and the coefficients are multiplied by the forecasts of each covariate. Returns a forecast conditional on forecasts of each covariate. Used to create a forecast for the present period or replicate a forecast made at a specific period in the past.

Usage

conditional_forc(lm_call, time_vec, ...)

Value

Forecast object that contains the conditional forecast.

Arguments

lm_call

Linear model call of the class lm.

time_vec

Vector of any class that is equal in length to the data in lm_call.

...

One or more forecasts of class Forecast, one forecast for each covariate in the linear model.

See Also

For a detailed example see the help vignette: vignette("lmForc", package = "lmForc")

Examples

x1_forecast <- Forecast(
   origin   = as.Date(c("2012-06-30", "2012-06-30", "2012-06-30", "2012-06-30")),
   future   = as.Date(c("2012-09-30", "2012-12-31", "2013-03-31", "2013-06-30")),
   forecast = c(4.14, 4.04, 4.97, 5.12),
   realized = NULL,
   h_ahead  = NULL
)

x2_forecast <- Forecast(
   origin   = as.Date(c("2012-06-30", "2012-06-30", "2012-06-30", "2012-06-30")),
   future   = as.Date(c("2012-09-30", "2012-12-31", "2013-03-31", "2013-06-30")),
   forecast = c(6.01, 6.05, 6.55, 7.45),
   realized = NULL,
   h_ahead  = NULL
)

date <- as.Date(c("2010-03-31", "2010-06-30", "2010-09-30", "2010-12-31",
                  "2011-03-31", "2011-06-30", "2011-09-30", "2011-12-31", 
                  "2012-03-31", "2012-06-30"))
y  <- c(1.09, 1.71, 1.09, 2.46, 1.78, 1.35, 2.89, 2.11, 2.97, 0.99)
x1 <- c(4.22, 3.86, 4.27, 5.60, 5.11, 4.31, 4.92, 5.80, 6.30, 4.17)
x2 <- c(10.03, 10.49, 10.85, 10.47, 9.09, 10.91, 8.68, 9.91, 7.87, 6.63)
data <- data.frame(date, y, x1, x2)

conditional_forc(
  lm_call = lm(y ~ x1 + x2, data),
  time_vec = data$date,
  x1_forecast, x2_forecast
)