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.
- 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, Mac, and Linux.
Requirements: GAUSS/GAUSS Light version 8.0 or higher.