ordinary least squares
[rols]=ols2(y,x)
* y = dependent variable vector (nobs x 1)
* x = independent variables matrix (nobs x nvar)
* rols = a tlist with
- rols('meth') = 'ols'
- rols('y') = y data vector
- rols('x') = x data matrix
- rols('nobs') = nobs
- rols('nvar') = nvars
- rols('beta') = bhat
- rols('yhat') = yhat
- rols('resid') = residuals
- rols('vcovar') = estimated variance-covariance matrix of beta
- rols('sige') = estimated variance of the residuals
- rols('sige') = estimated variance of the residuals
- rols('ser') = standard error of the regression
- rols('tstat') = t-stats
- rols('pvalue') = pvalue of the betas
- rols('dw') = Durbin-Watson Statistic
- rols('condindex') = multicolinearity cond index
- rols('prescte') = boolean indicating the presence or absence of a constant in the regression
- rols('rsqr') = rsquared
- rols('rbar') = rbar-squared
- rols('f') = F-stat for the nullity of coefficients other than the constant
- rols('pvaluef') = its significance level