Name

fernandez — Temporal disaggregation using the Fernandez method

CALLING SEQUENCE

[y,res]=fernandez(namey,arg1,…,argn)

PARAMETERS

Input

• nameY = a time series, a real (nx1) vector or a

• string equal to the name of a time series or a (nx1) 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

[y,res] = fernandez(Y,x,'ta=-1');
 
Example taken from function fernandez_d. Provides quarterly disaggregation of (22x1) 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