average_forecast {midasr} | R Documentation |
Average forecasts of MIDAS models
Description
Average MIDAS model forecasts using specified weighting scheme. Produce in-sample and out-of-sample accuracy measures.
Usage
average_forecast(
modlist,
data,
insample,
outsample,
type = c("fixed", "recursive", "rolling"),
fweights = c("EW", "BICW", "MSFE", "DMSFE"),
measures = c("MSE", "MAPE", "MASE"),
show_progress = TRUE
)
Arguments
modlist |
a list of |
data |
a list with mixed frequency data |
insample |
the low frequency indexes for in-sample data |
outsample |
the low frequency indexes for out-of-sample data |
type |
a string indicating which type of forecast to use. |
fweights |
names of weighting schemes |
measures |
names of accuracy measures |
show_progress |
logical, TRUE to show progress bar, FALSE for silent evaluation |
Details
Given the data, split it to in-sample and out-of-sample data. Then given the list of models, reestimate each model with in-sample data and produce out-of-sample forecast. Given the forecasts average them with the specified weighting scheme. Then calculate the accuracy measures for individual and average forecasts.
The forecasts can be produced in 3 ways. The "fixed"
forecast uses model estimated with in-sample data. The "rolling"
forecast reestimates model each time by increasing the in-sample by one low frequency observation and dropping the first low frequency observation. These reestimated models then are used to produce out-of-sample forecasts. The "recursive"
forecast differs from "rolling"
that it does not drop observations from the beginning of data.
Value
a list containing forecasts and tables of accuracy measures
Author(s)
Virmantas Kvedaras, Vaidotas Zemlys
Examples
set.seed(1001)
## Number of low-frequency observations
n<-250
## Linear trend and higher-frequency explanatory variables (e.g. quarterly and monthly)
trend<-c(1:n)
x<-rnorm(4*n)
z<-rnorm(12*n)
## Exponential Almon polynomial constraint-consistent coefficients
fn.x <- nealmon(p=c(1,-0.5),d=8)
fn.z <- nealmon(p=c(2,0.5,-0.1),d=17)
## Simulated low-frequency series (e.g. yearly)
y<-2+0.1*trend+mls(x,0:7,4)%*%fn.x+mls(z,0:16,12)%*%fn.z+rnorm(n)
mod1 <- midas_r(y ~ trend + mls(x, 4:14, 4, nealmon) + mls(z, 12:22, 12, nealmon),
start=list(x=c(10,1,-0.1),z=c(2,-0.1)))
mod2 <- midas_r(y ~ trend + mls(x, 4:20, 4, nealmon) + mls(z, 12:25, 12, nealmon),
start=list(x=c(10,1,-0.1),z=c(2,-0.1)))
##Calculate average forecasts
avgf <- average_forecast(list(mod1,mod2),
data=list(y=y,x=x,z=z,trend=trend),
insample=1:200,outsample=201:250,
type="fixed",
measures=c("MSE","MAPE","MASE"),
fweights=c("EW","BICW","MSFE","DMSFE"))