midasr (version 0.9)

select_and_forecast: Create table for different forecast horizons

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"),
  ...
)

Value

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

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

Author

Virmantas Kvedaras, Vaidotas Zemlys

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.

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")
## )