Multivariate temporal disaggregation with transversal constraint
[y,res]=difonzo1(Y,x,z,ta,s,typemod)
* Y = a (N x M) real vector
---> M series of low frequency data with N observations
* x = a (n x M) real vector
---> M series of high frequency data with n observations
* z = (n x 1) ---> high frequency transversal constraint with nz obs.
* ta = type of disaggregation
-ta = 1 ---> sum (flow)
-ta = 0 ---> average (index)
-ta = i ---> i th element (stock) ---> interpolation
* s = number of high frequency data points for each low frequency data points
-s = 4 ---> annual to quarterly
-s = 12 ---> annual to monthly
-s = 3 ---> quarterly to monthly
* typemod = model for the high frequency innovations
-ypemod = 'wn' ---> multivariate white noise
-typemod = 'rw' ---> multivariate random walk
* y = High frequency estimate
* res = a results tlist with:
- res('meth') = 'Multivariate difonzo'
- res('typemod') = type of the model for the high frequency innovations
- res('ta') = type of disaggregation
- res('nobs_lf') = nobs. of low frequency data
- res('nobs_hf') = nobs. of high-frequency data
- res('pred') = number of extrapolations
- res('s') = frequency conversion between low and high freq.
- res('diff') = Degree of differencing
- res('y') = high frequency estimate
- res('y_lf') = low frequency data
- res('indicator') = high frequency indicators
- res('transversal') = data for the transversal constraint
- res('y_dt') = high frequency estimate: standard deviation
- res('resid') = high frequency residuals
- res('resid_U') = low frequency residuals
- res('beta') = estimated model parameters
- res('sd') = standard deviation of the estimated model parameters