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chowlin1

Temporal disaggregation using the Chow-Lin method

CALLING SEQUENCE

[y,res]=chowlin1(namey,arg1,...,argn)

PARAMETERS

Input

* 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:

  - ta = -1 ---> sum (flow)

  - ta = 0 ---> average (index)

  - ta = k ---> k th element ---> 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- a real (n x 1) vector

* delta = the increment used to evaluate the derivative- a string equal to the name of a time series or a (n x 1) real vector between quotes

* typemin = estimation method:

  -typemin='wls' ---> weighted least squares

  -typemin='llike' ---> maximum likelihood

 

Output

* y = High frequency estimate

* res = a results tlist with:

  - res('meth') = 'Chow-Lin'

  - 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_lf') = low frequency data

  - res('indicator') = high frequency indicators

  - res('y') = high frequency estimate

  - 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_hf') = high frequency residuals

  - res('resid_lf) = low frequency residuals

  - res('beta') = estimated model parameters

  - res('sd') = estimated model parameters: standard deviation

  - res('tstat') = estimated model parameters: t ratios

  - res('rho') = innovational parameter

  - res('aic') = Information criterion: AIC

  - res('bic') = Information criterion: BIC

  - res('typemin') = method of estimation

  - res('llike') = Log-likelihood at the estimated parameters

  - res('sigma') = Variance at the estimated parameters

DESCRIPTION

Temporal disaggregation using the Chow-Lin method (low level function only with vectors and all parameters must be given).

EXAMPLE

load(GROCERDIR+'\data\xesp.dat')
[y,res] = chowlin1(Y,x,-1,4,1e-5,'wls')
// Provides quarterly disaggregation of series Y through weighted least squares maximisation
// and with the annual data equal to the sum of corresponding quarterly data.
// The parameter used for the numerical derivative is equal to 1E-5.

AUTHOR

Eric Dubois 2005

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