bfl — Temporal disaggregation using the Boot-Feibes-Lisman method
[y,res]=bfl(nameY,ta,d,s)
nameY = a ts or a Nx1 vector of low frequency data or a string representing the name of a vector or a ts
ta = type of disaggregation
- ta=1 ---> sum (flow)
- ta=0 ---> average (index)
- ta=i ---> i th element (stock) ---> interpolation
d = objective function to be minimized: volatility of - - d=0 ---> levels
- d=1 ---> first differences
- d=2 ---> second differences
s = number of high frequency data points for each low frequency data point
- s= 4 ---> annual to quarterly
- s=12 ---> annual to monthly
- s= 3 ---> quarterly to monthly
y = High frequency estimate
res = a results tlist with:
- res('meth') = 'Boot-Feibes-Lisman'
- res('nobs_lf') = Number of low frequency data
- res('aggreg_mode') = Type of disaggregation
- res('s') = Frequency conversion
- res('diff') = Degree of differencing
- res('y_lf') = Low frequency data
- res('y') = High frequency estimate
- res('namey') = name of the low frequency data