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
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.
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.