我正在尝试运行一个简单的OLS回归，
%Demonstrate regression using the ols() function
%Step 1. Load the data
clear;
clc;
y=[1 2 3 4 5]
x=[1 2 3 4 5]
result=ols(y,x);
prt(result);
prt(result);
I am using James LeSages econometric tool box and the OLS function is here,
function results=ols(y,x)
% PURPOSE: least-squares regression
%---------------------------------------------------
% USAGE: results = ols(y,x)
% where: y = dependent variable vector (nobs x 1)
% x = independent variables matrix (nobs x nvar)
%---------------------------------------------------
% RETURNS: a structure
% results.meth = 'ols'
% results.beta = bhat (nvar x 1)
% results.tstat = t-stats (nvar x 1)
% results.bstd = std deviations for bhat (nvar x 1)
% results.yhat = yhat (nobs x 1)
% results.resid = residuals (nobs x 1)
% results.sige = e'*e/(n-k) scalar
% results.rsqr = rsquared scalar
% results.rbar = rbar-squared scalar
% results.dw = Durbin-Watson Statistic
% results.nobs = nobs
% results.nvar = nvars
% results.y = y data vector (nobs x 1)
% results.bint = (nvar x2 ) vector with 95% confidence intervals on beta
%---------------------------------------------------
% SEE ALSO: prt(results), plt(results)
%---------------------------------------------------
% written by:
% James P. LeSage, Dept of Economics
% University of Toledo
% 2801 W. Bancroft St,
% Toledo, OH 43606
% jlesage@spatial-econometrics.com
%
% Barry Dillon (CICG Equity)
% added the 95% confidence intervals on bhat
if (nargin ~= 2); error('Wrong # of arguments to ols');
else
[nobs nvar] = size(x); [nobs2 junk] = size(y);
if (nobs ~= nobs2); error('x and y must have same # obs in ols');
end;
end;
results.meth = 'ols';
results.y = y;
results.nobs = nobs;
results.nvar = nvar;
if nobs < 10000
[q r] = qr(x,0);
xpxi = (r'*r)eye(nvar);
else % use Cholesky for very large problems
xpxi = (x'*x)eye(nvar);
end;
results.beta = xpxi*(x'*y);
results.yhat = x*results.beta;
results.resid = y - results.yhat;
sigu = results.resid'*results.resid;
results.sige = sigu/(nobs-nvar);
tmp = (results.sige)*(diag(xpxi));
sigb=sqrt(tmp);
results.bstd = sigb;
tcrit=-tdis_inv(.025,nobs);
results.bint=[results.beta-tcrit.*sigb, results.beta+tcrit.*sigb];
results.tstat = results.beta./(sqrt(tmp));
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
results.rsqr = 1.0 - rsqr1/rsqr2; % r-squared
rsqr1 = rsqr1/(nobs-nvar);
rsqr2 = rsqr2/(nobs-1.0);
if rsqr2 ~= 0
results.rbar = 1 - (rsqr1/rsqr2); % rbar-squared
else
results.rbar = results.rsqr;
end;
ediff = results.resid(2:nobs) - results.resid(1:nobs-1);
results.dw = (ediff'*ediff)/sigu; % durbin-watson
当我尝试运行它时出现错误。
我在第 64 行收到错误。
这是一个明显的解决方案吗？该功能将非常有用，希望有人可以提供帮助。
https://www.spatial-econometrics.com/
这是计量经济学工具箱。
所以我的顶级代码是调用 ols 函数。
我认为这是由于没有在同一个 MATLAB 文件夹中拥有所有相关功能，这有什么区别吗？
你缺少一些依赖项。您的 ols 文件看起来像这个 matlab 中心贡献中的文件：https://ch.mathworks.com/matlabcentral/fileexchange/45093-time-frequency-generalized-phase-synchrony-for-eeg-signal-analysis
它甚至在描述中指出与您提供的来源相同的来源。它包含所有必需的依赖项。也许对于您的用例来说太多了...