View source: R/date.functions.R
mixed_freq_data | R Documentation |
Creates a MIDAS data structure for a single high-frequency covariate and a single low-frequency dependent variable.
mixed_freq_data(data.y, data.ydate, data.x, data.xdate, x.lag, y.lag, horizon, est.start, est.end, disp.flag = TRUE)
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 |
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. |
a list of MIDAS data structure.
Jonas Striaukas
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)
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