Nonlinear Systems Regression and Simulation
The MODEL procedure provides parameter estimation, simulation,
and forecasting of dynamic nonlinear simultaneous equation models.
The MODEL procedure includes the following features:
- nonlinear regression analysis for systems of simultaneous equations,
including weighted nonlinear regression
- full range of parameter estimation methods including
- nonlinear ordinary least squares (OLS)
- nonlinear seemingly unrelated regression (SUR)
- nonlinear two-stage least squares (2SLS)
- nonlinear three-stage least squares (3SLS)
- iterated SUR
- iterated 3SLS
- generalized method of moments (GMM)
- nonlinear full information maximum likelihood (FIML)
- supports dynamic multi-equation nonlinear models of any
size or complexity
- uses the full power of the SAS programming language
for model definition, including left-hand side expressions
- hypothesis tests of nonlinear functions of the parameter estimates
- linear and nonlinear restrictions of the parameter estimates
- bounds imposed on the parameter estimates
- computation of estimates and standard errors of nonlinear functions of
the parameter estimates
- estimation and simulation of Ordinary Differential Equations (ODE's)
- vector autoregressive error processes and
polynomial lag distributions easily specified
for the nonlinear equations
- variance modeling (ARCH, GARCH, and others)
- computes goal-seeking solutions of nonlinear systems to find input values
needed to produce target outputs
- dynamic, static, or n-period-ahead-forecast simulation modes
- simultaneous solution or single equation solution modes
- Monte Carlo simulation using parameter estimate covariance
and across-equation residuals covariance matrices or user
specified random functions
- a variety of diagnostic statistics including
- model R2 statistics
- general Durbin-Watson statistics and exact p-values
- asymptotic standard errors and T tests
- first stage R2 statistics
- covariance estimates
- collinearity diagnostics
- simulation goodness-of-fit statistics
- Theil inequality coefficient decompositions
- Theil relative change forecast error measures
- heteroscedasticity tests
- Godfrey test for serial correlation
- Chow tests
- block structure and dependency structure analysis
for the nonlinear system
- listing and cross reference of fitted model
- automatic calculation of needed derivatives using
exact analytic formula
- efficient sparse matrix methods used for model solution; choice
of other solution methods
- model definition, parameter estimation, simulation, and
forecasting may be performed interactively in a single SAS session or
models can also be stored in files and reused and combined
in later runs
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
