You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: rossi.m
    path of file: ./repos/malini/Codes/FC_EC_calculation code/DynamicFC/jplv7/ts_aggregation
    
    the code of the file until where you have to start completion: function res = rossi(Y,x,z,ta,s,type);
% PURPOSE: Multivariate temporal disaggregation with transversal constraint
% -----------------------------------------------------------------------------
% SYNTAX: res = rossi(Y,x_ini,z,ta,s,type);
% -----------------------------------------------------------------------------
% OUTPUT: res: a structure
%         res.meth  = 'Multivariate Rossi';
%         res.N     = Number of low frequency data
%         res.n     = Number of high frequency data
%         res.pred  = Number of extrapolations (=0 in this case)
%         res.ta    = Type of disaggregation

    
function res = rossi(Y,x,z,ta,s,type);
% PURPOSE: Multivariate temporal disaggregation with transversal constraint
% -----------------------------------------------------------------------------
% SYNTAX: res = rossi(Y,x_ini,z,ta,s,type);
% -----------------------------------------------------------------------------
% OUTPUT: res: a structure
%         res.meth  = 'Multivariate Rossi';
%         res.N     = Number of low frequency data
%         res.n     = Number of high frequency data
%         res.pred  = Number of extrapolations (=0 in this case)
%         res.ta    = Type of disaggregation
%         res.s     = Frequency conversion
%         res.y     = High frequency estimate
%         res.et    = Elapsed time
% -----------------------------------------------------------------------
% INPUT: Y:     NxM ---> M series of low frequency data with N observations
%        x:     nxM ---> M series of high frequency data with n observations
%        z:     nx1 ---> high frequency transversal constraint
%        ta: type of disaggregation
%            ta=1 ---> sum (flow)
%            ta=2 ---> average (index)
%            ta=3 ---> last element (stock) ---> interpolation
%            ta=4 ---> first element (stock) ---> 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
%        type: univariate temporal disaggregation procedure used to compute
%        preliminary estimates
%            type = 1 ---> Fernandez
%            type = 2 ---> Chow-Lin 
%            type = 3 ---> Litterman
% -----------------------------------------------------------------------
% LIBRARY: aggreg, vec, desvec, fernandez, chowlin, litterman
% -----------------------------------------------------------------------
% SEE ALSO: denton, difonzo, mtd_print, mtd_plot
% -----------------------------------------------------------------------
% REFERENCE: Rossi, N. (1982)"A note on the estimation of disaggregate 
% time series when the aggregate is known", Review of Economics and Statistics, 
% vol. 64, n. 4, p. 695-696.
% Di Fonzo, T. (1994) "Temporal disaggregation of a system of 
% time series when the aggregate is known: optimal vs. adjustment methods",
% INSEE-Eurostat Workshop on Quarterly National Accounts, Paris, december.

% written by:
% Enrique M. Quilis
% Instituto Nacional de Estadistica
% Paseo de la Castellana, 183
% 28046 - Madrid (SPAIN)

t0 = clock;

%--------------------------------------------------------
%       Initial assignment 

x_ini = x; 
clear x;

%--------------------------------------------------------
%       Preliminary checking

[N,M] = size(Y);
[n,m] = size(x_ini);
[nz,mz] = size(z);

if ((M ~=  m) | (n ~= s*N) | (nz ~=  n) | (mz ~=  1) | (nz ~= s*N))
   error (' *** INCORRECT DIMENSIONS *** ');
else
   clear m nz mz;
end