prandom.m

用于做统计分析的matlab 程序包,各种基本统计分析的函数都有

function results = prandom(y,index,x)
% PURPOSE: performs Random Effects Estimation for Panel Data
%          (for balanced or unbalanced data)
%---------------------------------------------------------------------------------------
% USAGE:  results = prandom(y,index,x)
% where:    y     = a (nobs x neqs) matrix of all of the individual's observations 
%						 vertically concatenated. This matrix must include in the firt
%						 column the dependent variable, the independent variables must follow
%						 accordingly.	
%				index = index vector that identifies each observation with an individual
%                  e.g. 1  (first 2 observations  for individual # 1)
%                       1
%                       2  (next  1 observation   for individual # 2)
%                       3  (next  3 observations  for individual # 3)
%                       3 
%                       3
%			    x    = optional matrix of exogenous variables, 
%						  dummy variables. 	
%						(NOTE: constant vector automatically included)
%---------------------------------------------------------------------------------------
%RETURNS a structure
% results.meth  = 'prandom'
% results.nobs  = nobs, # of observations
% results.nvar  = nvars, # of variables
% results.nid   =  # of observations per individual
% results.beta  = bhat 
% results.tstat = t-statistics 
% results.tprob = t-probabilities
% results.resid = residuals 
% results.yhat  = predicted values 
% results.y     = actual values 
% results.sige  = e'e/(n-k)
% results.rsqr  = r-squared
% results.rbar  = r-squared adjusted
% results.sigew = sigma^2 e, "within estimator"
% results.sigem = sigma^2 1/ total observations, "between estimator"
% results.sigu2 = sigma^2 u
% results.xmat  = matrix of independent variables
% results.iintc = individual intercepts
% results.idy   = identity of individual;
% results.time  = time elapsed during the procedure
% results.crconst = correction of the constant term
%----------------------------------------------------------------------------------------

%Written by:
% Carlos Alberto Castro
% National Planning Department
% Bogota, Colombia
% Email: [e-mail] 

%****************************************************************************************
% NOTE: James P. LeSage provided corrections on the creation and use of the index
%       vector used to identify the the individual's observations.
%****************************************************************************************

t0 = clock;

results.meth = 'prandom';

[nobs equ]= size(y);

nx = 0;

if nargin == 3 
[nobs2 nx] = size(x);
 if (nobs2 ~= nobs)
 error('nobs in x-matrix not the same as y-matrix');
 end;
end;

results.nobs = nobs;

% creation of the id matrix using the vector index

nindiv = length(unique(index));
id = zeros(nindiv,3);    
id(:,1) = unique(index); 
for i=1:nindiv
    id(i,2) = length(find(index == i));
end;
id(:,3) = cumsum(id(:,2));

results.nid = id(:,2);
results.idy =id(:,1);
results.crconst = 0; %correction of the constant term

% tranformation of all the variables used
% the variables are expressed as deviations from the individual means
[n u]= size(id);

i=1;

for j=1:n

	while i<=id(j,3),
   
   ytemp= y(i:id(j,3),:);
   
   medias(j:id(j,1),:)= mean(ytemp);
   madj(i:id(j,3),:)= ytemp-(ones(id(j,2),1)*mean(ytemp));
       
   i= i+ id(j,2);      
   
	end;
end;

  
 yw= madj;

% form x-matrix
if nx 
xmatw = [yw(:,2:equ) x];
else
xmatw = [yw(:,2:equ)];
end;

[nobs3 nvarw]= size(xmatw); 

% run OLS

 resw = ols(yw(:,1),xmatw);
 results.residw = resw.resid;     % resids 
 siguw = resw.resid'*resw.resid; %sse

 results.sigew = siguw/(nobs-n-nvarw);    % sigma e
   
  
% Between estimation
  [obsm nvam]= size(medias);
  indepm= [	medias(:,2:nvam) ones(obsm,1)];	

  resb = ols(medias(:,1),indepm);
  
  results.betam = resb.beta;      % bhats of the Between estimation
  results.sigem = resb.sige;       % sigma 1/ total observations.
  
  % Sigma u
  
  results.sigu2 = results.sigem-(results.sigew/mean(id(j,2))); 

  if (results.sigu2 < 0)
 error('Sigma u is negative, use an alternative estimator');
 end;
 
 % tranformation of all the variables used
 % the variables are expressed as weighted deviations from the individual means
 % taking into account the groupwise heteroscedasticity
 
i=1;

for j=1:n

	while i<=id(j,3),
   
   ytempr = y(i:id(j,3),:);
 

   alfa(j,:)= 1-((sqrt(results.sigew))/(sqrt(id(j,2)*results.sigem)));
   mtr(i:id(j,3),:)= ytempr-(alfa(j,:)*(ones(id(j,2),1)*mean(ytempr)));
       
   i= i+ id(j,2);      
   
	end;
end;

 yr= mtr;
 
% tranformation of the constant
const= ones(nobs,1)-(max(alfa)*ones(nobs,1));
 
% form x-matrix
if nx 
xmatr = [yr(:,2:equ) x const];
else
xmatr = [yr(:,2:equ) const];
end;

[nobs4 nvars]= size(xmatr); 
results.nvar  = nvars;
results.xmat  = xmatr;

% run OLS

res = ols(yr(:,1),xmatr);
results.beta  = res.beta;      % bhats
results.tstat = res.tstat;     % t-stats
 %compute t-probs
      tstat = zeros(nvars,1);
      tstat = res.tstat;
      tout = tdis_prb(tstat,nobs-nvars);
results.tprob = tout;          % t-probs
results.resid = res.resid;     % resids 
    sigu = res.resid'*res.resid; %sse
results.yhat = res.yhat;       % yhats
results.y    = yr(:,1);           % actual y
results.rsqr = res.rsqr;       % r-squared
results.rbar = res.rbar;       % r-adjusted
results.sige = res.sige;       % sigma e
 
% form x-matrix
if nx 
xmat = [y(:,2:equ) x ones(nobs,1)];
else
xmat = [y(:,2:equ) ones(nobs,1)];
end;

% individual intercepts
  
   results.resdo = y(:,1)-(xmat*(results.beta));    

i=1;

for j=1:n

	while i<=id(j,3),
       
   results.iintc(j,:)= (results.sigu2/(id(j,2)*results.sigem))*sum(results.resdo(i:id(j,3),:));
          
   i= i+ id(j,2);      
   
	end;
end;

results.time = etime(clock,t0); % time elapsed during the procedure