mv_into_mat: MIDAS variable matrix transformation

In rumidas: Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and MEM-MIDAS

MIDAS variable matrix transformation

Description

Implements the transformation of the MIDAS variable into a matrix, whose dimension is (K+1) \times N, where K is the number of lagged realizations to consider and N is the length of the variable x.

Usage

mv_into_mat(x, mv, K, type)

Arguments

x	Variable according to which the MIDAS term has to be aligned. It must be an 'xts' object.
mv	MIDAS variable, observed each period t. It must be an 'xts' object.
K	Number of (lagged) realizations of the MIDAS variable to consider.
type	The frequency of the period of observations for the MIDAS variable. It can be 'weekly', 'monthly', 'quarterly' or 'yearly'.

Value

The resulting matrix has as many rows as the number of lagged realizations (plus one) of the MIDAS variable to consider, and as many columns as the length of x.

Examples

require(xts)
# weekly frequency
# obtain weekly MIDAS variable after daily aggregation
RV_weekly_sum<-apply.weekly(rv5^0.5,sum) #realized volatility
# then allocate correctly the information
RV_weekly<-as.xts(coredata(RV_weekly_sum),seq(as.Date("2000-01-10"), 
by = "week", length.out = length(RV_weekly_sum)))
# use mv_into_mat 
mv_into_mat(sp500['2002/2003-12-26'],diff(RV_weekly['/2003-12']),K=4,type="weekly")

# monthly frequency
r_t<-sp500['2005/2010']
mv_into_mat(r_t,diff(indpro),K=12,type="monthly")

# quarterly frequency
RV_quarterly_sum<-apply.quarterly(rv5,sum)
RV_quarterly<-as.xts(coredata(RV_quarterly_sum),seq(as.Date("2000-04-01"), 
by = "quarter", length.out = length(RV_quarterly_sum)))
mv_into_mat(sp500['2004/2010'],diff(RV_quarterly),K=10,type="quarterly")

# yearly frequency
RV_yearly_sum<-apply.yearly(rv5,sum)
RV_yearly<-as.xts(coredata(RV_yearly_sum),seq(as.Date("2001-01-01"), 
by = "year", length.out = length(RV_yearly_sum)))
mv_into_mat(sp500['2006/2010'],diff(RV_yearly),K=2,type="yearly")

rumidas documentation built on April 4, 2025, 1:01 a.m.