|
Linear
Regression MT
The Linear Regression
MT application
module is a set of procedures for estimating single equations or a
simultaneous system of equations. It allows constraints on
coefficients, calculates het-con standard errors, and includes
two-stage least squares, three-stage least squares, and seemingly
unrelated regression. It is thread-safe and takes advantage of
structures found in later versions of GAUSS.
Features
- Calculates
heteroskedastic-consistent standard errors, and performs both influence
and collinearity diagnostics inside the ordinary least squares routine
(OLS)
- All
regression procedures can be run at a specified data range
- Performs
multiple linear hypothesis testing with any form
- Estimates
regressions with linear restrictions
- Accommodates
large data sets with multiple variables
- Stores
all important test statistics and estimated coefficients in an
efficient manner
- Both
three-stage least squares and seemingly unrelated regression can be
estimated iteratively
- Thorough
Documentation
- The
comprehensive user's guide includes both a well-written tutorial and an
informative reference section. Additional topics are included to enrich
the usage of the procedures. These include:
- Joint
confidence region for beta estimates
- Tests
for heteroskedasticity
- Tests
of structural change
- Using
ordinary least squares to estimate a translog cost function
- Using
seemingly unrelated regression to estimate a system of cost share
equations
- Using
three-stage least squares to estimate Klein's Model I
Platform:
Windows, LINUX, Mac, and UNIX.
Requirements:
GAUSS/GAUSS Engine/GAUSS Light version 6.0 or higher.
|