Generalized least squares
[rgls]=gls(omega,namey0,arg1,...,argn)
* omega = a (nobs x nobs) variance matrix
* namey0 = a time series, a real (n x 1) vector or a string equal to the name of a time series or a (n x 1) real vector between quotes
* arg1,...,argn = arguments which can be:
. a time series
. a real (nxp) vector
. a string equal to the name of a time series or a (nxp) real vector between quotes
. the string 'noprint' if the user doesn't want to print the results of the regression
. 'dropna' if the user wants to remove the NA values from the data
. 'saturate(x)' if the user wants to test breaks with impulse indicator saturation at the size x
. 'noprint' if the user does not want to print the estimation results
* rgls = a results tlist with
- rgls('meth') = 'gls' or 'saturated gls'
- rgls('y') = y data vector
- rgls('x') = x data matrix
- rgls('nobs') = # observations
- rgls('nvar') = # variables
- rgls('beta') = bhat
- rgls('yhat') = yhat
- rgls('resid') = residuals
- rgls('vcovar') = estimated variance-covariance matrix of beta
- rgls('sige') = estimated variance of the residuals
- rgls('sigu') = sum of squared residuals
- rgls('ser') = standard error of the regression
- rgls('tstat') = t-stats
- rgls('pvalue') = pvalue of the betas
- rgls('dw') = Durbin-Watson Statistic
- rgls('condindex') = multicolinearity cond index
- rgls('prescte') = boolean indicating the presence or absence of a constant in the regression
- rgls('llike') = the log-likelihood
- rgls('aic')= the Akaike information criterion
- rgls('bic')= the Schwarz information criterion
- rgls('hq')= the Hannan-Quinn information criterion
- rgls('rsqr') = rsquared
- rgls('rbar') = rbar-squared
- rgls('f') = F-stat for the nullity of coefficients other than the constant
- rgls('pvaluef') = its significance level
- rgls('like') = log-likelihood of the regression
- rgls('prests') = boolean indicating the presence or absence of a time series in the regression
- rgls('namey') = name of the y variable
- rgls('namex') = name of the x variables
- rgls('dropna') = boolean indicating if NAs have been dropped
- rgls('bounds') = if there is a timeseries in the regression, the bounds of the regression
- rgls('nonna') = vector indicating position of non-NAs
- rgls('saturation significance level') = significance level used to keep the dummies
- rgls('significant dummies') = the remaining dummies after testing
