select_and_forecast: Create table for different forecast horizons

In midasr: Mixed Data Sampling Regression

Description

Creates tables for different forecast horizons and table for combined forecasts

Usage

select_and_forecast(
  formula,
  data,
  from,
  to,
  insample,
  outsample,
  weights,
  wstart,
  start = NULL,
  IC = "AIC",
  seltype = c("restricted", "unrestricted"),
  test = "hAh_test",
  ftype = c("fixed", "recursive", "rolling"),
  measures = c("MSE", "MAPE", "MASE"),
  fweights = c("EW", "BICW", "MSFE", "DMSFE"),
  ...
)

Arguments

formula	initial formula for the
data	list of data
from	a named list of starts of lags from where to fit. Denotes the horizon
to	a named list for lag selections
insample	the low frequency indexes for in-sample data
outsample	the low frequency indexes for out-of-sample data
weights	names of weight function candidates
wstart	starting values for weight functions
start	other starting values
IC	name of information criteria to choose model from
seltype	argument to modsel, "restricted" for model selection based on information criteria of restricted MIDAS model, "unrestricted" for model selection based on unrestricted (U-MIDAS) model.
test	argument to modsel
ftype	which type of forecast to use.
measures	the names of goodness of fit measures
fweights	names of weighting schemes
...	additional arguments for optimisation method, see midas_r

Details

Divide data into in-sample and out-of-sample. Fit different forecasting horizons for in-sample data. Calculate accuracy measures for individual and average forecasts.

Value

a list containing forecasts, tables of accuracy measures and the list with selected models

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys

Examples

### Sets a seed for RNG ###
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)
##Do not run
## cbfc<-select_and_forecast(y~trend+mls(x,0,4)+mls(z,0,12),
## from=list(x=c(4,8,12),z=c(12,24,36)),
## to=list(x=rbind(c(14,19),c(18,23),c(22,27)),z=rbind(c(22,27),c(34,39),c(46,51))),
## insample=1:200,outsample=201:250,
## weights=list(x=c("nealmon","almonp"),z=c("nealmon","almonp")),
## wstart=list(nealmon=rep(1,3),almonp=rep(1,3)),
## IC="AIC",
## seltype="restricted",
## ftype="fixed",
## measures=c("MSE","MAPE","MASE"),
## fweights=c("EW","BICW","MSFE","DMSFE")
## )

midasr documentation built on Feb. 23, 2021, 5:11 p.m.