midasml (version 0.0.6)

qtarget.sort_midasml: High-dimensional mixed frequency data sort function

Description

Sorts high-dimensional mixed frequency data for quarterly target variable.

Usage

qtarget.sort_midasml(y.data, x.macro.data = NULL, x.real.time = NULL,
  x.quarterly_group = NULL, x.lag = NULL, legendre_degree, horizon, 
  macro_delay = 1, est.start, est.end, standardize = TRUE, group_ar_lags = FALSE, 
  real_time_predictions = FALSE, disp.flag = TRUE)

Arguments

y.data

response variable data.

x.macro.data

macro data which is not real-time, i.e. is used with publication delay defined in .

x.real.time

real-time data.

x.quarterly_group

quarterly data currently taken as real-time data.

x.lag

single value or vector of size of the total number of variables defining the number of lags for each high-frequency variable in .

legendre_degree

single value or vector of size of the total number of variables defining the polynomial degree for each each high-frequency variable in .

horizon

forecast horizon relative to date column in high-frequency time units.

macro_delay

number of months that macro series in are delayed.

est.start

estimation start date, taken as the first ... .

est.end

estimation end date, taken as the last ... . Remainig data after this date is dropped to out-of-sample evaluation data.

standardize

TRUE/FALSE to standardize high-frequneyc covariates in high-frequency units.

group_ar_lags

TRUE/FALSE to group AR lags.

real_time_predictions

TRUE/FALSE in case real-time data is used for predictions

disp.flag

display flag to indicate whether or not to display obtained MIDAS data structure in console.

Value

MIDAS covariates and group memberships based on desired specification.

Details

Examples

# NOT RUN {
data(macro_midasml)
est.start <- as.Date("1990-12-01")
est.end <- as.Date("2017-03-01")
rgdp.data <- macro_midasml$rgdp.data
rgdp.data <- rgdp.data[rgdp.data$DATE<=as.Date("2017-06-01"),]
qtarget.sort_midasml(y.data = rgdp.data, x.macro.data = macro_midasml$md.data, 
         x.real.time = macro_midasml$text.data, x.quarterly_group = macro_midasml$survey.data, 
         x.lag = 12, legendre_degree = 3, 
         horizon = 1, macro_delay = 1, est.start, est.end, 
         standardize = TRUE, group_ar_lags = FALSE, disp.flag = FALSE)
# }