Dieses Modul ist ein Satz von Prozeduren zur Abschätzung von einzelnen Gleichungen oder eines gleichmäßig verteilten Systems von Gleichungen mittels OLS. Es ermöglicht Randbedingungen für Koeffizienten, berechnet den het-con Standardfehler sowie die minimierten quadratischen Fehler. Linear Regression MT ist multithreadfähig und verwendet Strukturen.

Spezielle Features

• Berechnungen von Klein Modellen nach der 3-Stufen kleinste Quadrat-Methode
• lineare Kostenteilungsmodelle
• beziehungslose Regressionen
• Tests auf strukturelle Veränderungen
• Tests auf Heteroskedastizität
• Erstellung von Vertrauensbereichen für Betaschätzungen
• Überprüfungen auf Kolinearitäten

Details:

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.

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, Mac, Linux and Solaris.

Requirements: GAUSS/GAUSS Light version 8.0 or higher.