oos_vintage_forc: Out-of-sample linear model forecast conditioned on vintage...

In lmForc: Linear Model Forecasting

Description

oos_vintage_forc takes a linear model call, a vector of time data associated with the linear model, a forecast for each covariate in the linear model, and an optional integer number of past periods to estimate the linear model over. For each period in the vintage forecasts, coefficients are estimated with data up to the current period minus the number of periods specified in estimation_window. If estimation_window is left NULL then the linear model is estimated with all available data up to the current period. Coefficients are then multiplied by vintage forecast values. Returns an out-of-sample forecast conditional on vintage forecasts that would have been available at the forecast origin. Optionally returns the coefficients used to create each forecast. Replicates the forecasts that a linear model would have produced in real time.

Usage

oos_vintage_forc(
  lm_call,
  time_vec,
  ...,
  estimation_window = NULL,
  return_betas = FALSE
)

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.
...	Set of forecasts of class Forecast, one forecast for each covariate in the linear model.
estimation_window	Integer representing the number of past periods that the linear model should be estimated over in each period.
return_betas	Boolean, selects whether the coefficients used in each period to create the forecast are returned. If TRUE, a data frame of betas is returned to the Global Environment.

Value

Forecast object that contains the out-of-sample forecast.

See Also

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

Examples

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)

x1_forecast_vintage <- Forecast(
   origin   = as.Date(c("2010-09-30", "2010-12-31", "2011-03-31", "2011-06-30")),
   future   = as.Date(c("2011-09-30", "2011-12-31", "2012-03-31", "2012-06-30")),
   forecast = c(6.30, 4.17, 5.30, 4.84),
   realized = c(4.92, 5.80, 6.30, 4.17),
   h_ahead  = 4L
)

x2_forecast_vintage <- Forecast(
   origin   = as.Date(c("2010-09-30", "2010-12-31", "2011-03-31", "2011-06-30")),
   future   = as.Date(c("2011-09-30", "2011-12-31", "2012-03-31", "2012-06-30")),
 forecast = c(7.32, 6.88, 6.82, 6.95),
 realized = c(8.68, 9.91, 7.87, 6.63),
 h_ahead  = 4L
) 

oos_vintage_forc(
  lm_call = lm(y ~ x1 + x2, data),
  time_vec = data$date,
  x1_forecast_vintage, x2_forecast_vintage,
  estimation_window = 4L,
  return_betas = FALSE
)

oos_vintage_forc(
  lm_call = lm(y ~ x1 + x2, data),
  time_vec = data$date,
  x1_forecast_vintage, x2_forecast_vintage
)

lmForc documentation built on Jan. 4, 2022, 1:11 a.m.