maximum likelihood estimation of an autocorrelated model
[result]=olsar1_1(y,x,optfunc,opt_optim)
* y = a real (n,1) vector or a
* x = a real (n,k) matrix
* optfunc = 'optimg' if the user wants to use the optim optimisation function (default: optim)
* opt_optim = a tlist, collecting the options to the optimisation function
* rolsar1 = a results tlist with
- rolsar1('meth') = ' ar(1) maximum likelihood'
- rolsar1('y') = y data vector
- rolsar1('x') = x data matrix
- rolsar1('nobs') = # observations
- rolsar1('nvar') = # variables
- rolsar1('beta') = bhat
- rolsar1('yhat') = yhat
- rolsar1('resid') = residuals
- rolsar1('vcovar') = estimated variance-covariance matrix of beta
- rolsar1('sige') = estimated variance of the residuals
- rolsar1('sigu') = sum of squared residuals
- rolsar1('ser') = standard error of the regression
- rolsar1('tstat') = t-stats
- rolsar1('pvalue') = pvalue of the betas
- rolsar1('dw') = Durbin-Watson Statistic
- rolsar1('condindex') = multicolinearity cond index
- rolsar1('prescte') = boolean indicating the presence or absence of a constant in the regression
- rolsar1('rsqr') = rsquared
- rolsar1('rbar') = rbar-squared
- rolsar1('f') = F-stat for the nullity of coefficients other than the constant
- rolsar1('pvaluef') = its significance level
- rolsar1('prests') = boolean indicating the presence or absence of a time series in the regression
- rolsar1('rho') = estimated first order autocorrelation of residuals
- rolsar1('trho') = its Student t
- rolsar1('like') = log-likelihood of the regression