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: rvarf_g.m
    path of file: ./repos/malini/Codes/FC_EC_calculation code/DynamicFC/jplv7/var_bvar
    
    the code of the file until where you have to start completion: function ylevf = rvarf_g(y,nlag,nfor,begf,prior,ndraw,nomit,x);
% PURPOSE: Gibbs forecasts for a Bayesian vector autoregressive 
%          model using the random-walk averaging prior 
%          y = A(L) Y + X B + E, E = N(0,sige*V), 
%          V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)
%          c = R A(L) + U, U = N(0,Z), random-walk averaging prior
%          diffuse prior on B is used          
%---------------------------------------------------
% USAGE:  yfor = rvarf_g(y,nlag,nfor,begf,prior,ndraw,nomit,x)
% WHERE:    y    = an (nobs x neqs) matrix of y-vectors (in levels)
%           nlag = the lag length
%           nfor = the forecast horizon
%           begf = the beginning date of the forecast   
%          prior = a structure variable
%               prior.rval, r prior hyperparameter, default=4
%               prior.m,    informative Gamma(m,k) prior on r
%               prior.k,    informative Gamma(m,k) prior on r 
%               prior.w,    an (neqs x neqs) matrix containing prior means
%                           (rows should sum to unity, see below)
%               prior.freq = 1 for annual, 4 for quarterly, 12 for monthly
%               prior.sig  = prior variance hyperparameter (see below)
%               prior.tau  = prior variance hyperparameter (see below)
%               prior.theta = prior variance hyperparameter (see below)                
%          ndraw = # of draws
%          nomit = # of initial draws omitted for burn-in                 
%           x    = an (nobs x nx) matrix of deterministic variables
%                  (in any form, they are not altered during estimation)
%                  (constant term automatically included)                  
% priors for important variables:  N(w(i,j),sig) for 1st own lag
%                                  N(  0 ,tau*sig/k) for lag k=2,...,nlag               
% priors for unimportant variables: N(w(i,j) ,theta*sig/k) for lag 1 
%                                   N(  0 ,theta*sig/k)    for lag k=2,...,nlag  
% e.g., if y1, y3, y4 are important variables in eq#1, y2 unimportant

    
function ylevf = rvarf_g(y,nlag,nfor,begf,prior,ndraw,nomit,x);
% PURPOSE: Gibbs forecasts for a Bayesian vector autoregressive 
%          model using the random-walk averaging prior 
%          y = A(L) Y + X B + E, E = N(0,sige*V), 
%          V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)
%          c = R A(L) + U, U = N(0,Z), random-walk averaging prior
%          diffuse prior on B is used          
%---------------------------------------------------
% USAGE:  yfor = rvarf_g(y,nlag,nfor,begf,prior,ndraw,nomit,x)
% WHERE:    y    = an (nobs x neqs) matrix of y-vectors (in levels)
%           nlag = the lag length
%           nfor = the forecast horizon
%           begf = the beginning date of the forecast   
%          prior = a structure variable
%               prior.rval, r prior hyperparameter, default=4
%               prior.m,    informative Gamma(m,k) prior on r
%               prior.k,    informative Gamma(m,k) prior on r 
%               prior.w,    an (neqs x neqs) matrix containing prior means
%                           (rows should sum to unity, see below)
%               prior.freq = 1 for annual, 4 for quarterly, 12 for monthly
%               prior.sig  = prior variance hyperparameter (see below)
%               prior.tau  = prior variance hyperparameter (see below)
%               prior.theta = prior variance hyperparameter (see below)                
%          ndraw = # of draws
%          nomit = # of initial draws omitted for burn-in                 
%           x    = an (nobs x nx) matrix of deterministic variables
%                  (in any form, they are not altered during estimation)
%                  (constant term automatically included)                  
% priors for important variables:  N(w(i,j),sig) for 1st own lag
%                                  N(  0 ,tau*sig/k) for lag k=2,...,nlag               
% priors for unimportant variables: N(w(i,j) ,theta*sig/k) for lag 1 
%                                   N(  0 ,theta*sig/k)    for lag k=2,...,nlag  
% e.g., if y1, y3, y4 are important variables in eq#1, y2 unimportant
%  w(1,1) = 1/3, w(1,3) = 1/3, w(1,4) = 1/3, w(1,2) = 0                                              
% typical values would be: sig = .1-.3, tau = 4-8, theta = .5-1  
%---------------------------------------------------
% NOTES: - estimation is carried out in annualized growth terms because 
%          the prior means rely on common (growth-rate) scaling of variables
%          hence the need for a freq argument input.
%        - constant term included automatically  
%---------------------------------------------------
% RETURNS:
%  yfor = an nfor x neqs matrix of level forecasts for each equation
%---------------------------------------------------------------
% ---------------------------------------------------    
% SEE ALSO: bvarf_g, becmf_g, recmf_g, rvar_g
% ---------------------------------------------------
% References: LeSage and Krivelyova (1998) 
% ``A Spatial Prior for Bayesian Vector Autoregressive Models'',
% forthcoming Journal of Regional Science, (on http://www.econ.utoledo.edu)
% and
% LeSage and Krivelova (1997) (on http://www.econ.utoledo.edu)
% ``A Random Walk Averaging Prior for Bayesian Vector Autoregressive Models''

% written by:
% James P. LeSage, Dept of Economics
% University of Toledo
% 2801 W. Bancroft St,
% Toledo, OH 43606
% jpl@jpl.econ.utoledo.edu

[nobs neqs] = size(y);
% find # observations up to forecast period
nmin = min(nobs,begf-1);

nx = 0;

% error checking on input
if ~isstruct(prior)
    error('rvarf_g: must supply the prior as a structure variable');
end