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Grocer >> Time series disaggregation > fernandez

fernandez

Temporal disaggregation using the Fernandez method

CALLING SEQUENCE

[y,res]=fernandez(namey,arg1,...,argn)

PARAMETERS

Input

* 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

 

Output

* 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

DESCRIPTION

Temporal disaggregation using the Fernandez method of multivariate temporal disaggregation (high level function with vectors, matrices or ts and the possibility of default parameters).

EXAMPLE

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.

AUTHOR

Eric Dubois 2005

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