conditional_forc_general: General model forecast conditioned on input forecasts

In lmForc: Linear Model Forecasting

General model forecast conditioned on input forecasts

Description

conditional_forc_general takes a model function, a prediction function, input data for estimating the model, and a vector of time data associated with the model. The model is estimated once over the entire sample period and the model parameters are then combined with the input forecasts to generate a forecast. Returns a forecast conditional on forecasts of each parameter. Used to create a forecast for the present period or replicate a forecast made at a specific period in the past.

Usage

conditional_forc_general(
  model_function,
  prediction_function,
  data,
  time_vec,
  ...
)

Arguments

model_function	Function that estimates a model using the data input.
prediction_function	Function that generates model predictions using model_function and data arguments. Note* that the data argument passed to the prediction_function takes the form of a data.frame with a number of columns equal to the number of input vintage forecasts passed by the user. The prediction_function needs to be able to take this input format and generate a prediction based on it.
data	Input data for estimating the model.
time_vec	Vector of any class that represents time and is equal in length to the length of realized and data.
...	Set of forecasts of class Forecast, one forecast for each parameter in the linear model.

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

# Estimation Data.
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", "2012-09-30", "2012-12-31",
                  "2013-03-31", "2013-06-30", "2013-09-30", "2013-12-31"))
y  <- c(1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0)
x1 <- c(8.22, 3.86, 4.27, 3.37, 5.88, 3.34, 2.92, 1.80, 3.30, 7.17, 3.22, 3.86,
    4.27, 3.37, 5.88, 3.34)
x2 <- c(4.03, 2.46, 2.04, 2.44, 6.09, 2.91, 1.68, 2.91, 3.87, 1.63, 4.03, 2.46, 
    2.04, 2.44, 6.09, 2.91)
dataLogit <- data.frame(date, y, x1, x2)

# Parameter Forecasts.
x1_forecastLogit <- Forecast(
   origin   = as.Date(c("2013-12-31", "2013-12-31", "2013-12-31", "2013-12-31")),
   future   = as.Date(c("2014-03-31", "2014-06-30", "2014-09-30", "2014-12-31")),
   forecast = c(2.11, 6.11, 6.75, 4.30),
   realized = NULL,
   h_ahead  = NULL
)

x2_forecastLogit <- Forecast(
   origin   = as.Date(c("2013-12-31", "2013-12-31", "2013-12-31", "2013-12-31")),
   future   = as.Date(c("2014-03-31", "2014-06-30", "2014-09-30", "2014-12-31")),
   forecast = c(1.98, 7.44, 7.86, 5.98),
   realized = NULL,
   h_ahead  = NULL
)

# Forecasting Function.
conditional_forc_general(
    model_function = function(data) {glm(y ~ x1 + x2, data = data, family = binomial)},
    prediction_function = function(model_function, data) {
        names(data) <- c("x1", "x2")
        as.vector(predict(model_function, data, type = "response"))
    }, 
    data = dataLogit,
    time_vec = dataLogit$date,
    x1_forecastLogit, x2_forecastLogit
) 

lmForc documentation built on Sept. 11, 2024, 8:14 p.m.