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LSQ @COEF vector #params @SES vector #params @T @SSR vector vector #params #eqs @YMEAN vector #eqs @SDEV vector #eqs @S vector #eqs @DW vector #eqs @RSQ @COVU @W vector #eqs matrix #eqs*#eqs matrix #eqs*#eqs @COVT matrix #eqs*#eqs @VCOV matrix @RES matrix #obs*#eqs @FIT matrix #obs*#eqs #par*#par Vector of estimated values of the parameters Vector of standard errors of the estimated parameters Vector of corresponding t-statistics Sum of squared residuals for each of the equations, stored in a vector Means of the dependent variable for each of the equations Standard deviations of the dependent variable for each of the equations. Standard errors for each of the equations Durbin-Watson statistics for each equation R-squared for each equation Residual covariance matrix The inverse square root of COVU, the upper triangular weighting matrix Covariance matrix of the transformed (weighted) residuals. This is equal to the number of observations times the identity matrix if estimation is by maximum likelihood Estimated variance-covariance of estimated parameters. Residuals = actual - fitted values of the dependent variable. Matrix of fitted values of the dependent variables Normal LSQ output begins with a listing of the equations. The model is checked for linearity in the parameters (which simplifies the computations). A message is printed if linearity is found and LSQ does not iterate because it is unnecessary. The amount of working space used by LSQ is also printed this number can be compared with the amount printed at the end of the run to see how much extra room you have if you wish to expand the model. 250
