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Linear
Regression
The Linear Regression 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.
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 and UNIX.
Requirements: GAUSS/GAUSS Light version 3.2 or higher.
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