mixed_freq_data: MIDAS data structure

In midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data

MIDAS data structure

Description

Creates a MIDAS data structure for a single high-frequency covariate and a single low-frequency dependent variable.

Usage

mixed_freq_data(data.y, data.ydate, data.x, data.xdate, x.lag, y.lag, 
  horizon, est.start, est.end, disp.flag = TRUE)

Arguments

data.y	n by 1 low-frequency time series data vector.
data.ydate	n by 1 low-frequency time series date vector.
data.x	m by 1 high-frequency time series data vector.
data.xdate	m by 1 high-frequency time series date vector.
x.lag	number of high-frequency lags to construct in high-frequency time units.
y.lag	number of low-frequency lags to construct in low-frequency time units.
horizon	forecast horizon relative to data.ydate date in high-frequency time units.
est.start	estimation start date, taken as the first ... .
est.end	estimation end date, taken as the last ... . Remaining data after this date is dropped to out-of-sample evaluation data.
disp.flag	display flag to indicate whether or not to display obtained MIDAS data structure in console.

Value

a list of MIDAS data structure.

Author(s)

Jonas Striaukas

Examples

data(us_rgdp)
rgdp <- us_rgdp$rgdp
payems <- us_rgdp$payems
payems[-1, 2] <- log(payems[-1, 2]/payems[-dim(payems)[1], 2])*100
payems <- payems[-1, ]
rgdp[-1, 2] <- ((rgdp[-1, 2]/rgdp[-dim(rgdp)[1], 2])^4-1)*100
rgdp <- rgdp[-1, ]
est.start <- as.Date("1990-01-01")
est.end <- as.Date("2002-03-01")
mixed_freq_data(rgdp[,2], as.Date(rgdp[,1]), payems[,2], 
  as.Date(payems[,1]), x.lag = 9, y.lag = 4, horizon = 1, 
  est.start, est.end, disp.flag = FALSE)

midasml documentation built on April 29, 2022, 9:06 a.m.