Gaussx incorporates a full featured set of professional, state of the art, econometric routines that run under GAUSS. These tools can be used within Gaussx, both in research and in teaching. Alternatively, since the GAUSS source is included, individual econometric routines can be extracted and integrated in stand-alone GAUSS programs.

The Windows version is fully integrated with GAUSS using a floating toolbar, while the Unix version runs as a GAUSS application.

Gaussx runs from a menu in which the user specifies the input and output files, and the directories for data. A command file is written using commands similar to SAS or TSP:

OLS y c x1 x2;

Gaussxthen executes this command file and the results are shown on the screen, and written to an output file, which is available for viewing after the end of execution. Thus Gaussx replicates the edit/run/view cycle that seems to be most efficient in running econometric analysis. And since this cycle is menu driven, the learning curve is almost zero. One can run Gaussx without knowing GAUSS at all. However, since any Gauss statement can be used within GAUSS, all the power of GAUSS is available. In addition, all the tools most commonly needed for econometric analysis are provided, at whatever level is required.For a comprehensive review of what's in Gaussx, have a look at Features.

Gaussx can use any editor, including GAUSS; however a custom editor, GsxEdit, is included. GsxEdit is similar to GAUSS 3.6, and provides Gaussx context sensitive help. It can also be used as a stand alone editor.

The current versions of

Gaussx are:

Gaussx for Windows 10.10 -- requires GAUSS for Windows 6.0 or higher.

Gaussx for Unix 10.03 -- requires GAUSS for Unix 6.0 or higher.

Gaussx for Mac 10.03 -- requires GAUSS for Mac 6.0 or higher.

Recent additions (see flyer) included in version 10.0:

- Gauss 11, 12 & 13 support
- x64 version - the Gaussx package now contains both 32 and 64bit versions.
- Gaussian copulas - COPULA
- Rank correlation matrix - CORR
- Random draws from correlated multivariate distributions - MVRND
- Stepwise regression
- Latin Hypercube Sample - LHS
- Statlib update - Gaussx now supports over 60 statistical distributions.
Additions included in version 9.0:

- Panel estimation
- 15 Survival processes from exponential to Weibull, including censoring
- Non-parametric survival
- Cox proportional hazards model
- Random number generator for any CDF
- Function inversion
- Print enhancement
- Normality tests
Additions included in version 8.0:

- N-way analysis of variance for fixed, random or mixed models. Nested and interaction effects, variance components.
- 10 Survival processes from exponential to Weibull, including censoring
- Survival estimates and bounds - survival, inverse survival, hazard and cumulative hazard.
- Feasible / Exact local Whittle estimation.
- Non-linear count estimation using Poisson and negative binomial distributions.
- Tabulate enhancements
- Forecast of predicted value and standard errors for variables that are functions of estimated parameters
- Filter enhancements
- Batch mode.
Additions included in version 7.0:

- Partial Least Squares
- Response Surface Methodology (multiple response optimization)
- Consumer surplus and deadweight loss
- Linear Programming
- Murphy Topel two step estimation for all ML and/or NLS models
- Global optimization and Nelder-Meade optimization algorithms
- Bayesian estimation of Poisson, SURE and MNP models
- Bayesian convergence diagnostics
- Cubic Interpolation
- GAUSSPlot support
- Maple 9.5 and 10 support
- Correlation dimension and Lyapunov exponent
- Parametric tests - Anova, Bartlett, KPSS, Jarque-Bera, Hansen, Newey-West, Welch. Non-Parametric location tests - sign, Wilcoxon, Walsh, Mann-Whitney, Friedman, Conover, Kruskal-Wallis, Mood
- Non-Parametric scale tests - Levene, Brown-Forsythe, O'Brien.
- Durbin-Watson probabilities.
- ML estimation of Normal processes
- Data generating process, Wiener and fBm processes
- Non-linear Ordered Logit and Probit processes
- Enhanced statistics for discrete choice models
- Gini coefficients
- Matrix I/O enhancements
A detailed discussion of

what's newis provided in recent changes.

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GAUSS is a registered trademark of Aptech Systems, Inc.

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