Temporal disaggregation using the Chow-Lin method
[y,res]=chowlin1(namey,arg1,...,argn)
* 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
* 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
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. | ![]() | ![]() |