function [b, ts, r2]=ols_reg(y,x,c)
%OLSH(X, Y)      	runs a linear regression of the vector y on
%                  	the matrix x and reports the results. 
%c=1 includes the constant  



[t, k] = size(x);         % t = # of obs., k = # of regressors
[m, n] = size(y);
if n > 1
  error('y must be a vector.');
end
if m ~= t
  error('y and x must have the same number of rows.');
end
if k > t
  error('More parameters than observations.');
end

df = t - k;                                 % degrees of freedom 

if c== 1
  x= [ones(t,1) x];
else
  x=x;
end


%--- compute the moment matrix, coefficient vector and residuals ---%
b    = inv(x'*x)*x'*y;                       % coefficients
u    = y - x*b;                              % residuals 
tss  = (t-1) * std(y)^2;                     % total sum of squares
rss   = u'*u;                                % residual sum of squares 
ess   = tss - rss;                           % explained sum of squares 


%--- compute the variance/covariance matrix of b ---%
vc = rss * inv(x'*x) / df;

%--- compute various regression statistics ---%
se = sqrt(diag(vc));                       % standard errors of b 
ts = b ./ se;                              % t statistics 
r2 = ess / tss;                            % R-squared 
dw = sum( (u(2:t) - u(1:t-1)).^2 )' / rss; % Durbin-Watson statistic