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chowlin

Temporal disaggregation using the Chow-Lin method

CALLING SEQUENCE

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

PARAMETERS

Input

* nameY = a ts or a Nx1 vector of low frequency data or a string representing the name of a vector or a ts

* argi = an argument which can be

  - a time series

  - a real (nx1) vector

  - a string equal to the name of a time series or a (nx1) real vector between quotes

  - a list of such objects

  - the string 'divfq=n' where n is the number of high frequency data points for each low frequency data points

  - the string 'typemin=xxx' where xxx is the maximisation method (llike -default- or wls)

  - 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 'delta=x' where x is the increment used for numerical derivation

  - 'optfunc=optim' if the user wants to use the optim optimisation function (default: optimg)

  - 'opt_nelmead=crit,nitermax' with crit the value of the convergence criterion in the Nelder-Meade optimisation function and nitermax the maximum number of iterations (default = 'opt_nelmead=2*%eps,1000')

  - 'opt_optim=opts' where opts are options for optim that can be entered after the starting value of the parameters (default = 'opt_optim=,''ar'',1e6,1e6'')

  - 'opt_convg=val' where val is the threshold on gradient norm (default = 'opt_convg=1e-5')

 

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

   - res('namey') = Name of the high frequency aggregate

   - res('namex') = Name of the low frequency indicators

DESCRIPTION

Temporal disaggregation using the Chow-Lin method (high level function with vectors, matrices or ts and the possibility of default parmaters)

EXAMPLE

load(GROCERDIR+'\data\xesp.dat')
[y,res] = chowlin('Y','x','ta=-1','typemin=wls')
 
// Example taken from function chowlin_d. Provides monthly disaggregation of series Y
// (in chowlin_d a vector, but it should also work if y were a ts)
// through weighted least squares maximisation and with the annual data equal to the sum of corresponding monthly data.

AUTHOR

Eric Dubois 2005

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