Creates tables for different forecast horizons and table for combined forecasts

1 2 3 4 5 | ```
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"), ...)
``` |

`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, |

`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 |

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.

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

Virmantas Kvedaras, Vaidotas Zemlys

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
### 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")
## )
``` |

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