Temporal disaggregation using the Fernandez method
[y,res]=fernandez1(Y,x,ta,s)
* Y = a (N x 1) vector of low frequency data
* x = a (n x p) matrix of high frequency indicators (without intercept)
* ta = type of disaggregation:
- n=-1 (default) ---> sum (flow)
- n=0 ---> average (index)
- n=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
* y = High frequency estimate
* res = a results tlist with:
- res('meth') = 'Fernandez'
- 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('p') = number of regressors (including intercept)
- res('y') = high frequency estimate
- res('y_lf') = low frequency data
- res('indicator') = high frequency indicators
- res('y_dt') = high frequency estimate: standard deviation
- res('y_up') = high frequency estimate: sd + sigma
- res('y_lo') = high frequency estimate: sd - sigma
- res('resid') = high frequency residuals
- res('resid_lf') = low frequency residuals
- res('beta') = estimated model parameters
- res('sd') = standard deviation of the estimated model parameters
- res('tstat') = estimated model parameters: t ratios
- res('aic') = Information criterion: AIC
- res('bic') = Information criterion: BIC
load(GROCERDIR+'\data\xesp.dat') [y,res] = fernandez1(Y,x,-1,4); // If Y and X are taken from the database GROCERDIR+'\macros\grocer\db\xesp.dat', // provides quarterly disaggregation of the (22x1) matrix Y // (representing annual Spanih exportations) with the (88x1) matrix x. // Annual series are built by summing quarterly series (3rd arg set to -1); // the order of aggregation is 4. | ![]() | ![]() |