Temporal disaggregation using the Fernandez method
[y,res]=fernandez(namey,arg1,...,argn)
* nameY = a time series, a real (n x 1) vector, a string equal to the name of a time series or a (n x1 ) real vector between quotes, representing the low frequency data that must be desaggregated
* argi = an argument which can be
- a time series
- a real (nxk) matrix
- a string matrix whose elements represent the names of a time series or a (nx1) real vector between quotes
- the string 'divfq=n' where n is the number of high frequency data points for each low frequency data points
- the string 'ta=n' where n is the aggregation type:
. n=-1 (default) ---> sum (flow)
. n=0 ---> average (index)
. n=i ---> i th element (stock) ---> interpolation
- the string 'typemod=n' where n is either 'wn' for the multivariare white noise model (default) or 'rw' for the multivariate random walk model
* 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
- res('namey') = Name of the low frequency data
- res('namex') = Name of the indicators
load(GROCERDIR+'\data\xesp.dat') [y,res] = fernandez(Y,x,'ta=-1'); // Example taken from function fernandez_d. // Provides quarterly disaggregation of (22 x 1) matrix Y (representing annual Spanih exportations) // with (88x1) matrix x. // Annual series are built by summing quarterly series (ta=-1); // the order of aggregation is determined by the function. | ![]() | ![]() |