Name

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

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