is_forc: In-sample linear model forecast

In lmForc: Linear Model Forecasting

Description

is_forc takes a linear model call and an optional vector of time data associated with the linear model. The linear model is estimated once over the entire sample period and the coefficients are multiplied by the realized values in each period of the sample. Returns an in-sample forecast conditional on realized values.

Usage

is_forc(lm_call, time_vec = NULL)

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.

Value

Forecast object that contains the in-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)

is_forc(
  lm_call = lm(y ~ x1 + x2, data),
  time_vec = data$date
)

is_forc(
  lm_call = lm(y ~ x1 + x2, data)
)

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