select_and_forecast function

Create table for different forecast horizons

Creates tables for different forecast horizons and table for combined forecasts

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

Returns

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

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")
## )

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys