Model estimation is only part of the data analysis. By 'post estimation,’ we mean the manipulation of model results along with other statistics and procedures.
All model estimates are 'recoverable.' Coefficients and asymptotic covariance matrices are retained and integrated into the matrix algebra package. Numerical values such as log likelihoods (individual observations and the model criterion function) and sums of squares are recoverable and useable, by name, for example, in testing hypotheses or computing diagnostic statistics.
Simulation and prediction
Estimated models may be used for simulations and for computing predictions, generalized residuals, and other functions for forecasting and specification analysis.
Programming tools for manipulating model results
Program tools are provided for manipulating results. One of the most useful is the WALD command which is used to compute nonlinear functions and asymptotic variances for nonlinear functions of model estimates. Programming the derivatives for the delta method is unnecessary.
Facilities are provided for bootstrap sampling cross sections and from panels. Thus, bootstrap standard errors can easily be computed. All model results can be retained for use in other program functions. Programming two step estimators is simple.