sar_gd2.m

计量工具箱

% PURPOSE: An example of using sar_g() on a large data set   
%          Gibbs sampling spatial autoregressive model                         
%---------------------------------------------------
% USAGE: sar_gd2 (see sar_gd for a small data set)
%---------------------------------------------------

clear all;
% NOTE a large data set with 3107 observations
% from Pace and Barry, takes around 150-250 seconds
load elect.dat;             % load data on votes
y =  log(elect(:,7)./elect(:,8));
x1 = log(elect(:,9)./elect(:,8));
x2 = log(elect(:,10)./elect(:,8));
x3 = log(elect(:,11)./elect(:,8));
latt = elect(:,5);
long = elect(:,6);
n = length(y); x = [ones(n,1) x1 x2 x3];
clear x1; clear x2; clear x3;
clear elect;                % conserve on RAM memory
n = 3107;
[junk W junk] = xy2cont(latt,long);
vnames = strvcat('voters','const','educ','homeowners','income');

result = sar(y,x,W); % maximum likelihood estimates
prt(result,vnames);

% do Gibbs sampling estimation
ndraw = 5000; 
nomit = 500;
prior.novi = 1; % homoscedastic model
result2 = sar_g(y,x,W,ndraw,nomit,prior);
result2.tflag = 'tstat';
prt(result2,vnames);


prior2.rval = 4; % heteroscedastic model
result3 = sar_g(y,x,W,ndraw,nomit,prior2);
result3.tflag = 'tstat';
prt(result3,vnames);

% compute posterior model probabilities
probs = model_probs(result2,result3);

% pretty print results
fprintf(1,'\n \n Bayesian model comparison \n');
in.cnames = strvcat('Posterior model probabilities');
in.rnames = strvcat('Models','Homoscedastic','Heteroscedastic');
mprint(probs,in);


for i=1:result2.nvar
[h1,f1,y1] = pltdens(result2.bdraw(:,i));
[h2,f2,y2] = pltdens(result3.bdraw(:,i));
plot(y1,f1,'.r',y2,f2,'.g');
legend('homoscedastic','heteroscedastic');
title(['posterior distribution for  beta ' num2str(i)]);
xlabel(vnames(i+1,:));
pause;
end;