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
load(GROCERDIR+'\data\xesp.dat') [y,res] = bfl(Y,-1,1,12); // Example taken from function bfl_d. Provides monthly disaggregation of series Y // (in bfl_d a vector, but it should also work if Y were a ts) // using first differences and with the annual data equal of the sum of corresponding monthly data. | ![]() | ![]() |