Econometric Analysis for GAUSS

Gaussx 5.0 for Windows is now shipping.

Description

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;
Gaussx then 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.

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:

5.0 (01) -- requires Gauss for Windows 3.5, 3.6, 4.0 or 5.0.
5.0 (01) -- requires Gauss for Unix 3.2.31 or above

Recent additions included in version 4.0:

Genentic algorithm
Census X12 smooting
Binary matrix I/O
Denoising using wavelets
String evaluation
Enhanced spreadsheet handling

Recent additions included in version 5.0:

Memory based storage
Fractionally Integrated GARCH
Feasible MNP for ranked and non ranked data (no covariance parameters)
EM estimation of a Mixed Gaussian distribution
Double-bounded dichotomous choice models
Markov switching models
Additions included in version 3.8

Cluster Analysis
Robust Estimation including Quantile Regression
Bayesian estimation using Markov Chain Monte Carlo
Trust Region methodology for optimization
Stochastic Volatility Model estimation
Additional diagnostic tests
Additional statistical distributions

Gaussx is Y2K compliant.