Linear Regression MT

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Linear Regression MT

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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,...
Overview

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.

Platform: Windows, Mac, and Linux.

Requirements: GAUSS/GAUSS Engine/GAUSS Light v8.0 or higher.

Features

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

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