Linear Regression MT ============================ Set of procedures for estimating single equations or a simultaneous system of equations in GAUSS. Description ---------------- 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**. Installation -------------- If you're interested in purchasing **LRMT** Please `contact us `_ to request pricing and installation information. If you already own **LRMT** , you can use the `GAUSS Package Manager `_ for quick download and installation. Requires GAUSS/GAUSS Engine/GAUSS Light v8.0 or higher. Key Features ------------------------------ Provides convenient and comprehensive tools for linear regression including: * Heteroskedastic-consistent standard errors. * 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. 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.