TStat - O F F I C I A L

SOFTWARE/GAUSS 11.0

Third Party Applications for GAUSS

The following products are developed by third party companies for use with GAUSS. These products are available only from Aptech Systems, Inc. Technical support is provided directly through the developer.

Further details on these products can be obtained via email from TStat S.r.l.

Product	Developer
Stat/Transfer 9.0: Data conversion utility for GAUSS	Circle Systems
Gaussx 9.0: Full set of professional econometrics routines	Econotron Software
LikPak 1.0: Likelihood procedures for GAUSS	Econotron Software
GUI Tools: for GAUSS	Econotron Software
IGX:Integrated GraphiX for GAUSS	Econotron Software
Mercury: Interface tools for GAUSS	Econotron Software
Mercury GE 6.1: Interface tools for GAUSS Engine	Econotron Software
Symbolic Tools: for GAUSS and GAUSS Engine	Econotron Software
SimGauss 2.1: Nonlinear simulation	Forward Software
SSATS 2.0: State Space Aoki Time Series	J. Dorfman
GENO 1.0: General Evolutionary Numerical Optimizer	APEX Research
SNAP 2.2: Social Network Analysis Procedures	Noah Friedkin
TSM 1.2: Time Series/Wavelets for Finance	Ritme Informatique
COINT 2.0: Co-integrated Systems	Sam Ouliaris and Peter C.B. Phillips

Stat/Transfer 9.0: Data conversion utility for GAUSS

The following product is developed by Circle Systems, a third party company. Technical support is provided directly through the developer.

Since 1986, Stat/Transfer has provided fast, reliable, and convenient data transfer for thousands of users, worldwide. Stat/Transfer knows about statistical data--it handles missing data, value and variable labels and all of the other details that are necessary to move as much information as is possible from one file format to another.

Stat/Transfer provides both an easy-to-use menu interface and a powerful batch facility. Whether you are moving a simple table from Excel to GAUSS or moving megabytes of survey data between statistical packages, Stat/Transfer will save you time and money.

Stat/Transfer Makes Your Data Instantly Usable.

Stat/Transfer is designed to simplify the transfer of statistical data between different programs.

Data generated by one program is often needed in another context, either for analysis, for cleaning and correction, or for presentation. However, not only the data must be transferred, but in addition it generally must be re-described for each program with additional information, such as variable names, missing values and value and variable labels. This process is not only time-consuming, it is error-prone. For those in possession of data sets with many variables, it represents a serious impediment to the use of more than one program.

Stat/Transfer removes this barrier by providing an extremely fast, reliable and automatic way to move data.

Stat/Transfer will automatically read statistical data in the internal format of one of the supported programs and will then transfer as much of the information as is present and appropriate to the internal format of another.

Stat/Transfer preserves all of the precision in your data by storing it internally in double precision format. However, on output, it will, where possible, automatically minimize the size of your output data set by intelligently choosing data storage types that are only as large as necessary to preserve the input precision. Stat/Transfer also allows precise and easy manual control over the storage format of your output variables, in case this is necessary.

In addition to converting the formats of variables, Stat/Transfer also processes missing values automatically.

Stat/Transfer can save hours and even days of manual labor, while at the same time eliminating error. Furthermore, you gain this speed and accuracy without losing flexibility, since Stat/Transfer allows you to select just the variables and cases you want to transfer.

In addition to the standard Windows interface, a command processor on Windows and on UNIX allows you to run a transfer in batch mode. This makes it straightforward to set up fully automatic batch procedures for repetitive tasks.

Stat/Transfer 10 reads and writes:

1-2-3
Access (Windows only)
ASCII - Delimited
ASCII - Fixed Format
dBASE and compatible formats
Epi Info
Excel
FoxPro
GAUSS
HTML Tables (write only)
JMP
LIMDEP
Matlab
Mineset
Minitab
NLOGIT
ODBC (Windows and Mac only)
OSIRIS (read only)
Paradox
Quattro Pro
R
SAS Data Files
SAS Value Labels
SAS CPORT (read only)
SAS Transport Files
S-PLUS
SPSS Data
SPSS Portable
Stata
Statistica (Windows only)
SYSTAT
Triple-S

Stat/Transfer's ODBC support allows you to also read and write to such relational databases as Oracle, Oracle, Sybase, Informix, and DB/2.

Find out about Stat/Transfer on these platforms:

32-bit Windows platforms
Mac OS X
Popular UNIX platforms

HP-9000 (HP-UX)
Intel/AMD (x86) (32-Bit Linux)
Intel/AMD (x86-64) (64-Bit Linux)
Sun SPARC (64-Bit Solaris)

Stat/Transfer for UNIX is command-driven and supports all of the file formats available in the Windows version except those, such as Access and ODBC, which require proprietary Microsoft components.

Gaussx 9.0: Full set of professional econometrics routines

Gaussx 9.0 Flyer

The following product is developed by Econotron Software, Inc. for use with GAUSS. Technical support is provided directly through the developer.

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.

Gaussx provides an environment that makes econometric programming a joy. For example,

ols y c x1 x2;

does ordinary least squares, while

mcmc z1 c z3 z4;
userproc = &g_tobit;

does a Bayesian estimation of a Tobit model using Markov Chain Monte Carlo.

Gaussx provides for linear and non-linear optimization with and without parameter constraints. A full set of econometric models, estimation routines and tests are supported, including: automatic differentiation, multivariate binomial probit, VARMA process, time series analysis, LDV models, GARCH models, exponential smoothing, X12 seasonal adjustment, non-parametric analysis, neural networks, wavelets, forecasting, Kalman filter, stochastic volatility, robust estimation, Bayesian estimation, cluster analysis, financial tools, econometric tests, Monte Carlo simulation and statistical distributions.

Gaussx is designed for econometricians and financial analysts and has been continuously upgraded over 15 years. The open source paradigm allows econometricians to use GaussX routines as templates for their own code.

New Features in Gaussx 9.0

PANEL
64-bit support
Normality tests - AD, SW, SF, PPC
Additional survival models
Cox Snell and martingale residuals
Nonparametric survival estimation - SURVIVAL
Cox proportional hazards model
Utility functions - PERMS, COMBS, INTERP2
Enhanced print option
Random number generation for any distribution - RNDGEN
Utility functions - Inverse hyperbolic functions
Transforms vector to a normal variate - NORMAL
Invert a function - INVERT
Tests of distributions - PIT (Probability Integral Transformation)

Available for Windows, Mac OS X, LINUX, UNIX (requires GAUSS for Windows 4.0 or higher).

LikPak 1.0: Likelihood procedures for GAUSS

LikPak 1.0 Flyer

The following product is developed by Econotron Software, Inc. for use with GAUSS. Technical support is provided directly through the developer.

LikPak 1.0 from Econotron Software consists of over 50 likelihood functions and examples for GAUSS. LikPak is designed to be used with GAUSS optimization packages such as Constrained Maximum Likelihood MT, Maximum Likelihood, and Maximum Likelihood MT.

LikPak has been designed to complement the optimization packages; it saves the programmer from having to write the likelihood and shows how the likelihood can be parameterized for a particular problem.

LikPak is designed to be used as a template; that is, select the example that is relevant to your problem and use that example as a starting point. The functions in LikPak corespond to the set of likelihoods currently used in economics, and each function is backed up with documentation describing typical parameterizations.

The source code is written in GAUSS and will run on any platform of GAUSS or the GAUSS Engine. See Processes and Utilities below for a list of processes and utilities included in LikPak. Full documentation and examples are provided for each function. See the online manual at www.econotron.com for details.

LikPak is available for Windows, Linux and Unix versions of GAUSS. You can visit Econotron Software's home page www.econotron.com for a full description of LikPak.

Processes and Utilities

AR Processes

ARFIMA - Autoregressive fractional integrated moving average process
ARIMA - Autoregressive integrated moving average process
ARMA - Autoregressive moving average process
VARMA - Vector autoregressive moving average process

Count Processes

NEGBIN - Negative binomial process
POISSON - Poisson process

Discrete Processes

DBDC - Double-bounded dichotomous choice process
FMNP - Feasible multinomial probit
LOGIT - Binomial logit process
MNL - Multinomial logit
MNP - Multinomial probit
ORDLGT - Ordered logit process
ORDPRBT - Ordered probit process
PROBIT - Binomial multivariate probit process

GARCH Processes

AGARCH - Asymmetric GARCH process
ARCH - Autoregressive conditional heteroscedastic process
EGARCH - Exponential GARCH process
FIGARCH - Fractionally integrated GARCH process
GARCH - GARCH process
IGARCH - Integrated GARCH process
MGARCH - Multivariate GARCH process
PGARCH - Power GARCH process
TGARCH - Truncated GARCH process

Statistical Processes

BETA - Beta processBETA Beta process
CAUCHY - Cauchy process
EXPON - Exponential process
F - F process
GAMMA - Gamma process
GUMBEL - Gumbel (largest extreme value) process
INVGAUSS - Inverse Gaussian process
LAPLACE - Laplace process
LEVY - Levy process
LOGISTIC - Logistic process
LOGLOG - Loglogistice process
LOGNORM - Log normal process
NORMAL - Normal process
PARETO - Pareto process
PEARSON - Pearson
SEV - Smallest extreme value process
STUDENTS_T - Student's T process
VONMISES - Von Mises process
WEIBULL - Weibull process

Other Processes

BOXCOX - BoxCox process
FPF - Frontier production function process
KALMAN - Kalman filter
MSM - Markov switching models
MVN - Multivariate normal process
NEURAL - Neural network process
NLS - Nonlinear least squares
NPE - Non parametric estimate
SV - Stochastic volatility process
TOBIT - Tobit process
WHITTLE - Local Whittle process

LikPak Utilities

CENSORED - Censored process
DGP - Data generation process
FILTER - Data filter
MROOT - Largest root
PDROOT - Positive definite test for smallest root
QDFN - Multivariate normal rectangular probabilities
RNDTN - Truncated multivariate normal random numbers
TRUNCATED - Truncated process

DS Utilities

dsDATA - Set data source
dsDATAGET - Retrieve data
dsOPTIONS - Set options
dsOPTIONGET - Retrieve options

PV Utilities

pvCLEAR - Clear parameter
pvCONST - Set parameter as inactive
pvGET - Retrieve parameter
pvGETMASK - Retrieve parameter mask
pvPARAM - Set parameter as active
pvSET - Set parameter
pvSETMASK - Set parameter mask

Available for Windows, Mac OS X, LINUX, UNIX (requires GAUSS for Windows 4.0 or higher).

GUI Tools: for GAUSS

GUI Tools 1.0 Flyer

The following product is developed by Econotron Software, Inc. for use with GAUSS. Technical support is provided directly through the developer

GUI Tools provides you with an interactive graphic user interface for GAUSS for Windows. This product enables the programmer to develop graphic-based dialog boxes and standard Windows controls for their end users to respond to, using both keyboard and mouse.

GUI tools is called from GAUSS with a minimum of programming. Typically, it is only necessary to specify a title, prompt, and the name of the control or GUI, followed by a one line call. GUI Tools does the rest to produce professional custom dialogs for you.

GUI Tools has three main components:

Standard Windows Controls
Standard Windows Dialogs
Custom GUIs

Standard Windows Controls

A set of standard Windows controls are included that can be called from GAUSS. These return the user input back to the control of GAUSS. Standard controls include message box, text, box, logon box, combo box, option buttons, and check box.

Standard Windows Dialogs

Also included are a set of standard Windows dialogs that can be called from GAUSS and which return the dialog results back to GAUSS. These include color select, file browse, font select, and print dialogs.

Custom GUIs

These are graphic interfaces that are custom designed for specific projects which are called from GAUSS. Each control in the GUI returns a value or a string to GAUSS. GUI Tools allows the programmer to create a custom interface using the standard graphic builder technique, the same technique used to build dialogs in Visual Basic.

How it works is as follows: a form is displayed in one window, and a control form in a second window. The programmer clicks on the required control to copy it to the form, and then, using the mouse, drags the control to the desired location and sizes it. A list of properties is provided for each control, which the programmer can set as desired.

GUI Reader is a freely available application that can be downloaded from the Econotron website. GUI Reader has the same functionality as GUI Tools, except that GUI description files cannot be created or modified. This product includes all the GUI Tools examples, online help, and manual. GUI Reader enables all the controls and dialogs provided in GUI Tools, as well as any user-defined GUI created with GUI Tools. These can be freely used in your GAUSS application.

Available for Windows 4.0 or higher.

IGX:Integrated GraphiX for GAUSS

The following products are developed by Econotron Software, Inc. for use with GAUSS. Technical support is provided directly through the developer

nteractive GraphiX (IGX) is a Windows graphics package specifically designed for GAUSS. IGX provides a high degree of control over a graphic environment while the graph is displayed, either interactively through menus using the mouse and keyboard, or through the use of GAUSS commands.

IGX allows you to:

Generate 20 different 2D and 3-D plot types such as scatter, line, bar, pie, radar, surface, contour, area, pyramid, candlestick, bubble, gantt.
Rotate the plot, and set shadow, depth and perspective.
Zoom and scroll plots
Use any Windows font; support for Greek and mathematical symbols, subscripts and superscripts.
Display, arrange, and print multiple windows, as overlays or inserts.
Use a wide range of annotation objects.
Use templates as a graphics style sheet.
Plot real time (streaming) data, as well as animations.
Export to 10 different output formats.
Use image processing tools to enhance your grap

IGX is designed to be run from GAUSS as part of a set of GAUSScommand file, from a template, interactively from the graph, or by running GAUSScommands while the graph is displayed. It is specifically designed for any GAUSS user who requires an alternative to PQG, and is available for the Windows version of GAUSS.

Available for Windows 4.0 or higher.

Mercury: Interface tools for GAUSS

The following product is developed by Econotron Software, Inc. for use with GAUSS. Technical support is provided directly through the developer

Mercury v5.1

Mercury consists of a set of functions that provide an interface with GAUSS. These function permit sending strings, values and data from the external application to, running GAUSS code or procedures, and returning the data back to the external application. Thread control is explicitly supported.

Mercury has four main components:

An Excel add-in that links an Excel Workbook to GAUSS. Data is sent to GAUSS, where it is processed, and the results are returned to the specified cells in the spreadsheet. Excel 95, 97, 2000 and XP are supported.

A library of interface functions for developers who need to link GAUSS to an external application using custom interfaces. Sample example code is provided for VB and C++ applications.

Windows clipboard support for GAUSS.
A demonstration project showing how GAUSS compliant DLLs are created.

Mercury GE 6.1: Interface tools for GAUSS Engine

Mercury flyer

The following product is developed by Econotron Software, Inc. for use with the Windows version of the GAUSS Engine and GRTE. Technical support is provided directly through the developer

Mercury GE consists of a set of functions that provide an interface with the GAUSS Engine. These functions permit sending strings, values and data from the external application, running GAUSS Engine code or procedures, and returning the data back to the external application. Thread control is explicitly supported.

Mercury has four main components:

An Excel add-in that links an Excel Workbook to the GAUSS Engine. Data is sent to the GAUSS Engine, where it is processed, and the results are returned to the specified cells in the spreadsheet. Excel 2000 and later are supported.
A library of interface functions for developers who need to link the GAUSS Engine to an external application using custom interfaces. Sample demonstration projects for both the GAUSS Engine and the GRTE are included for Excel, C (VC6, MFC, VC.NET and C#) and VB (VB6 and VB.NET).
Windows clipboard support for GAUSS.
A demonstration project showing how GAUSS Engine compliant DLLs are created.

Mercury GE is designed for developers who wish to use GAUSS Engine functionality within their applications, or who need to provide a custom front end for the GAUSS Engine. It is available on a royalty free basis to developers who wish to use the GRTE as part of an application.

New Features in v6.1

User control over error display.
Path control instead of using an environment variable.
Directory control.
Message and signal control.
Missing value capability.
Timer capability.

The ability to send and receive messages and signals while GAUSS is executing allows for interactive control while a job is being executed, as well as the capability to display the ongoing progress of a job, such as during optimization or simulation.

Mercury_Gauss requires Gauss for Windows 4.0 or higher. Mercury_GE requires the Gauss Engine for Windows 6.0 or higher.

Symbolic Tools: for GAUSS and GAUSS Engine

The concept behind Symbolic Tools is to augment the numeric and graphical capabilities of the GAUSS Mathematical & Statistical System (TM) and GAUSS Engine (TM) with additional types of mathematical functionality based on symbolic computations. These include:

Symbolic Algebra. This includes analytic differentiation and integration, automatic differentiation, as well as simplification.
Linear Algebra. This capability allows for the exact (as opposed to the numeric) evaluation of matrix forms, including inverses, determinants and eigenvalues.
Language Extension. This permits access to the full functionality of Maple, including all the mathematical functions and matrix forms, from within GAUSS, thus effectively extending the GAUSS language.
Precision. Numerical evaluation of functions can occur at any specified level of accuracy.

The computational work is carried out by the Maple kernel using the Open Maple API. Maple is a symbolic mathematics package developed at the University of Waterloo. Symbolic Tools provides for an interface between GAUSS and the Maple Kernel. This interface permits code to be evaluated symbolically in Maple, and the results returned to GAUSS, or to create a GAUSS proc based on Maple's symbolic results.

One of the main uses of Symbolic Tools is to enable GAUSS to undertake Automatic Differentiation. Optimization packages, such as Aptech's Maximum Likelihood, Constrained Maximum Likelihood, Optimization, and Nonlinear Equations GAUSS Applications, can use procedures that return the gradient and/or Hessian, instead of doing forward differencing. Thus, as a trivial example, if the function being optimized were Ln(b), then the analytic gradient would be 1/b, and the analytic Hessian -1/b. Symbolic Tools can create compiled procs for the analytic gradient or Hessian of a likelihood, on the fly. The time savings are impressive. Using Monte Carlo simulation of a Tobit model with 2000 observations and 11 parameters, the AD gradient took 10% of the time required for forward differences using gradp - ie. approximately a 10 fold speed improvement. Similar results were also obtained for the Hessian, with the additional advantage that the AD methodology generated much more precise estimates of the gradients and Hessian.

Examples:

Symbolic Tools manual is in a PDF format. Includes table of contents, examples, reference, and index.

Available for Windows GAUSS Mathematical & Statistical System v4.0+ for Windows or GAUSS Engine v4.0+ for Windows; Maple 9 or higher

SimGauss 2.1: Nonlinear simulation

The following product is developed by Forward Software, a third party company, for use with GAUSS. Technical support is provided directly through the developer.

A fully interactive nonlinear simulation module written in GAUSS, SimGauss provides a fast and easy way to simulate nonlinear differential equations and state-space systems, such as vehicle dynamics, biological systems and economic models. The module features extensive user control. GAUSS's Publication Quality Graphics provide exceptional ways to visualize your results. Comprehensive documentation and on-line help complete the package.

Features

The model simulation code is written in GAUSS. You can use GAUSS' high level mathematical functions such as probability density functions, FFTs, matrix inverse, eigenvalue/eigenvector and SVD functions to quickly simulate complex models and control algorithms.
Fully interactive. All the model variables can be displayed and modified from the GAUSS command level. The major simulation control variables can be displayed and edited using the SimGauss Control Panel.
8 Integration algorithms:- Euler, 2nd and 4th order Runge-Kutta, 2nd/3rd and 4th/5th order Runge-Kutta-Fehlberg, Richardson-Bulirsch-Stoer, Adams-Moulton and Gear's stiff method.
Change integration algorithms during the simulation and log the data at each integration step.
State vectors and vector derivative equations, e.g. d_x = Ax + Bu where d_x, x and u are vectors and A and B are matrices. Printing and plotting of state vectors is fully supported. You can run multiple versions of the model in one run using parameter vectors (see plot right).
Parameter optimization using GAUSS' optimization and non-linear equation solvers. With these procedures you can solve two-point boundary value problems and adjust the model's parameters to meet specifications or to match measured data.
Special SimGauss keywords simplify plotting of time and phase plots for both scalars and vectors.
Extensive simulation operators including:- Backlash, Bound, Deadband, Delay, Quantization, Limited Integration, Table Lookups and an algebraic equation solver.
Powerful user events. GAUSS keywords can be scheduled to execute at any time during the simulation to introduce disturbances, change parameter values, turn debugging on, etc.
Extensive error checking on model code, state dimensions and procedure arguments and a special debug command.
SimGauss can be extended by defining your own specialized procedures in the GAUSS language, or by including existing Fortran, C or Assembler code.
The simulation can be halted at any time and the entire workspace saved so you can continue later from the same point.
Publication Quality Graphics, high resolution (up to 4096x3120) 2D and 3D color graphics with hidden line removal, zoom and pan are available to enhance your reports.
Allows you to write your model fast, run it fast and analyze and plot the results fast, all from within the GAUSS environment.
On-line help and 160 page manual with numerous examples.

SSATS 2.0: State Space Aoki Time Series

The following product is developed by J. Dorfman, a third party developer, for use with GAUSS. Technical support is provided directly through the developer.

SSATS 2.0 is a set of preprogrammed GAUSS procedures that perform all the tasks necessary to and associated with the specification, estimation, and forecasting of multivariate state space time series models. A standard state space model takes the form:

y_t= Cz_t + e_t (observation equation)
z_t+1 = A_z t + Be_t (state equation)

where y_t is an (m x 1) vector of the time series to be modeled and/or forecast, z_tis the (n x 1) state vector, e_t is an (m x 1) vector of stochastic innovations (error terms), and A, B, and C are parameter matrices to be estimated.

Masanao Aoki developed a particularly successful algorithm to estimate such models based on the balanced representation and relying heavily on results from linear systems theory. SSATS 2.0 will let a researcher easily begin to implement the techniques laid out in Aoki's book, State Space Modeling of Time Series (Springer-Verlag, 1987, 1990).

SSATS will be useful to any researcher who is interested in empirical work on multivariate dynamic systems. SSATS is a valuable tool for anyone involved in the specification, estimation, and forecasting of multivariate (or univariate) time series models. The procedures can be used on their own, combined into a single command program, or used selectively in conjunction with other time series methods to aid in specification or forecast evaluation.

SSATS 2.0 provides procedures to easily accomplish such tasks as:

Scale and center data prior to estimation
Choose the model specification (model order of the time series),
Estimate the model coefficients A, B, and C
Estimate covariance matrices of parameter matrices, data series, errors, and states
Evaluate model specification with diagnostic tools
Produce in-sample and out-of-sample forecasts
Evaluate forecasting performance including a variety of summary statistics.

All of the forecasting evaluation procedures can be used with forecasts generated by any methods; they are not restricted to use with state space models. Similarly, the model specification procedures and statistical tests included can be used to identify the model order of a time series even if the researcher then estimates a VAR or VARMA model instead of a state space model.

The SSATS 2.0 procedure module comes with:

19 procedures
A complete user's guide containing descriptions and examples for all procedures
A primer on state space models, the Aoki estimation algorithm, and tips and guidance on how to successfully model and forecast multivariate time series using state space models
A sample program showing how to combine the procedures into a complete implementation of the procedures to specify a model, estimate it, produce forecasts, and evaluate the model's performance
A sample data set and demo output to allow researchers to insure that the programs are working properly on their systems.

Available for Windows, LINUX, UNIX, requires GAUSS Mathematical & Statistical System v3.2 and above.

GENO 1.0: General Evolutionary Numerical Optimizer

GENO 1.0 Flyer

The following product is developed by APEX Research Ltd. for use with GAUSS. Technical support is provided directly through the developer.

GENO is a numerical optimiser with exceptionally wide application. It may be used to solve uni- or multi-objective optimisation problems: the problem may be static or dynamic, linear or nonlinear, unconstrained or constrained (by equations or inequalities); in addition, any combination of the variables may assume real or discrete values.

GENO has been tested on many optimization problems from well-known test suites that cover a wide range of problem-types. GENO consistently out-performs many algorithms of its genre; and in terms of solution quality. Some practical examples solved include:

Pressure Vessel Design
Oligopolist Market Equilibrium
Efficient Portfolio Selection
Decentralised Economic Planning

GENO may be specialized for various classes of problems such as the general static optimization problem, the general dynamic optimization problem, the mixed integer problem, and the two-point boundary value problem, by mere choice of a few parameters. These properties are easily preset at the problem set-up stage of the solution process. GENO includes a quantization mechanism that significantly enhances the rate of convergence as well as the quality of the final solution. So if your model involves:

Static Optimization
Dynamic Optimization
Robust Optimization
Mixed Integer Optimization
Multi-objective Optimization
And more . . .

Available for Windows, UNIX, LINUX (requires GAUSS 6.0 or higher)

SNAP 2.2: Social Network Analysis Procedures

The following product is developed by Noah Friedkin, a third party developer, for use with GAUSS. Technical support is provided directly through the developer.

SNAP provides an integrated environment in which to conduct general mathematical/statistical investigations and social network analyses. It consists of four groups of procedures that operate on the value matrix of a network.

Create networks or perform basic operations; return adjacency matrices, profile similarities, quadratic placements, normalizations, and random networks.
Return information about a network or its parts. Network model of the social influence that is a special case for the mixed regressive- autoregressive model: Y = aWY+XB+E.
Network databases.

Available for Windows, requires GAUSS Mathematical and Statistical System v3.2 and above.

TSM 1.2: Time Series/Wavelets for Finance

The following product is developed by Ritme Informatique, a third party company for use with GAUSS. Technical support is provided directly through the developer.

TSM is a GAUSS library for time series modeling in both time domain and frequency domain and works in conjunction with the GAUSS Application - Optimization. It is primarily designed for the analysis and estimation of ARMA, VARX processes, state space models, fractional processes and structural models. To study these models, special tools have been developed like procedures for simulation, spectral analysis, Hankel matrices, etc. Estimation is based on the Maximum Likelihood principle and linear restrictions may be easily imposed.

TSM deals with vector ARMA(p,q) processes defined in the following form:

Following L�TKEPOHL [1991], several procedures enable one to get the VAR(1) representation, roots of the reverse characteristic polynomial, the pure AR and MA representations, the matrices of the response forecast errors and the orthogonal impulses (and those of the corresponding dynamic multipliers) and the forecast error variance decomposition matrices. Two types of estimation can be performed: Conditional Maximum Likelihood (based on REINSEL,[1993] and Exact Maximum Likelihood (based on ANSLEY and KOHN [1983]. Let q be the vector of parameters. Constrained maximum likelihood is obtained by imposing implicit linear restrictions in the form:

Related to ARMA processes (and to state space models), Hankel matrices may be computed. You can also determine the McMillan degree of an ARMA process (see Aoki [1987]).

State Space Models

Analysis and Estimation of state space models (SSM) are included in TSM. The SSM form corresponds to the one presented in HARVEY [1990]. Filtering, (fixed-interval) smoothing and maximum likelihood (with implicit linear restrictions) may be easily undertaken. For time invariant SSM, three additional procedures permit computing initial conditions, forecasting processes and solving the algebraic Riccati equation. Note that for structural models (local level, local linear trend, basic structural and cycle models), maximum likelihood can be performed in the frequency domain.

Spectral Analysis

TSM also contains spectral analysis procedures for the estimation of periodograms, cross-periodograms and coherencies, cross-amplitude spectra and phase spectra. Data windowing can be done in the frequency domain. The user has the choice between different lag window generators (rectangular, Hartlett, Daniell, Tukey, Parzen and Bartlett-Priestley) and may define his own generator. Note that there also exists a procedure for smoothing in the time domain, based on the Savitzky-Golay filter.

General maximum likelihood estimation can be undertaken. For ML estimation in the frequency domain (Whittle likelihood), special procedures are available. Linear restrictions may be imposed in this implicit, form-Jacobian, gradient and Hessian matrices (and information matrix in the frequency domain) allow one to easily perform Lagrange multiplier tests.

TSM also contains procedures for resampling and simulation, like bootstrap, surrogate data technique and kernel estimation.

New in Version 1.2

Version 1.2 of TSM contains 48 supplementary procedures which concern tools for state space models, special time series regression and Time-Frequency analysis of 1-D signal. New time series regression methods are implemented in TSM: Recursive Least Squares (Brown, Durbin and Evans, Journal of the Royal Statistical Society, 1975), Flexible Least Squares (Kalaba and Tesfatsion, Computers & Mathematics with Applications, 1989) and Generalized Flexible Least Squares (L�tkepohl and Herwartz, Journal of Econometrics, 1996). FLS and GFLS are methods for estimating the paths of time-varying coefficients. TSM contains also the GFLS filter and smoother for approximately linear systems (Kalaba and Tesfatsion, IEEE Transactions on Systems, Man and Cybernetics, 1990):

where yt is a m-dimension time series and at is the n-dimension state vector. The Generalized Method of Moments with implicit linear restrictions is now included.

TSM contains new tools to analyze state space models, for example impulse analysis, forecast error variance decomposition or theoretical Hankel matrix. We can now estimate parameters of multivariate model by maximum likelihood in the frequency domain, because TSM computes the multivariate periodogram and the spectral generating function of SSM. The algorithm for bootstrapping state space models (Stoffer and Wall, JASA, 1991) is implemented. We can now compute the gain matrices to obtain the innovations form representation:

This form is very useful to analyze the learning convergence.

Time-Frequency Analysis

Time-Frequency analysis (wavelet analysis and wavelet packet analysis) can be now performed with TSM. Different quadrature mirror filters are available: Coiflet, Daubechies, Haar and Pollen. Wavelet procedures concern the discrete wavelet transform (DWT), the inverse wavelet transform (IWT), wavelet decomposition coefficients subband tools (extraction, insertion, selection and split), the scalogram of the wavelet coefficients and the wavelet decomposition coefficients plot. Wavelet packet analysis is composed with nine procedures. It includes the wavelet packet transform (to generate packet tables), the inverse wavelet packet transform, the basis selection, best basis (the tree prunning algorithm of Coifman and Wickerhauser) and best level selections. Different information cost functions are considered: Shannon entropy, log energy and lp norm. And the user can define its own additive cost functions.

TSM also contains tools for signal denoising based on thresholding techniques: Soft, Hard and Semi-Soft wavelet shrinkages, quantile thresholding, etc. Denoised time series are easily obtained by signal reconstruction with the inverse wavelet transform or the inverse wavelet packet transform.

Several domains are concerned by Time-Frequency analysis: time series forecasting, density estimation, outlier testing, power spectrum estimation (Moulin, IEEE Transactions on Signal Processing, 1994), fractal signals (Wornell and Oppenheim, IEEE Transactions on Signal Processing, 1992), fractional processes, etc.

TSM 1.2 includes more than 95 procedures for:

ARMA processes
VARX processes
Spectral analysis
Maximum Likelihood Estimation, including: Time Domain Estimation, Frequency Domain Estimation for Univariate Processes
Univariate Models
State space models and the Kalman filter
Resampling and Simulation.
Estimation tools for time series analysis
Time-Frequency Analysis including: Quadrature mirror filters
Wavelet Analysis, with Periodic discrete wavelet transform, Wavelet Tools, Wavelet packet analysis with transform and basis functions, and Thresholding methods
Matrix operators

Extensively Illustrated and Documented

The package is extensively documented with over 230 pages in 2 volumes. More than 100 examples illustrate TSM routines. These examples are not just applications, but should be viewed as extensions of the library. They concern, for example, the optimal order of VAR models, the Kolmogorov-Smirnov statistic in the frequency domain, CUSUM and CUSUMsq tests or normality test for probit models.

TSM is written by Thierry Roncalli from the Economical Research and Analysis Laboratory of Bordeaux University, France, and published by Ritme Informatique.

Available for Windows, requires GAUSS Mathematical & Statistical Systems v3.2 and above AND GAUSS Application "Optimization v3.1.

COINT 2.0: Co-integrated Systems

The following product is developed by Sam Ouliaris and Peter C.B. Phillips, third party developers, for use with GAUSS. Technical support is provided directly through the developers.

A suite of econometric software for GAUSS users with a special focus on nonstationary time series, unit roots, cointegration and modern model selection methods for economists, econometricians, statisticians, engineers, forecasters and other users of time series methods.

Whether you are an economist doing empirical time series research, an econometrician in a forecasting unit, a professor teaching econometrics or a graduate student of economics or statistics, you need access to the latest regression methods for stationary and nonstationary time series.

COINT gives GAUSS users a huge library of scientific procedures for time series regression and model selection. Included are the latest techniques for unit root testing, cointegrating regression estimation, ARMA and VAR modeling with some unit roots, GMM and GIVE estimation with nonstationary data, and Bayesian as well as classical statistical methods for detecting unit roots and cointegration in economic time series.

COINT will enhance your research and teaching by giving you access to state-of-the-art times series methods and econometric techniques. Be more productive in GAUSS, work with the latest nonstationary regression methods and give presentations that utilize the latest features of GAUSS publication quality graphics.

COINT 2.0 gives you:

Unit Root Tests - Have a wide range of procedures at your fingertips to test for the presence of a unit root. Use the latest data-based tests that employ model selection and kernel estimation with automatic bandwidth selectors. Test your data for stationarity as well as nonstationarity.
Cointegration Tests - Test for cointegration and find the dimension of the cointegration space using data-driven residual based tests and likelihood ratio tests.
Tabulated Critical Values - Have at your disposal a complete set of tabulated critical values for unit root and cointegration tests. COINT has an automated search facility that delivers critical values whenever test statistics are computed.
Cointegrating Regression - Choose a routine for estimating the parameters of a cointegrated system. COINT has a large selection of methods: FM-OLS and its latest enhancements including FM-GMM, FM-GIVE and FM-VAR; reduced rank regression methods; canonical cointegrating regression; spectral regression; and structural stability tests for cointegrating regression.
Bayesian Unit Root Analysis - Do a Bayesian analysis of nonstationarity for your time series and cointegrating regression residuals. COINT gives you graphical procedures to plot marginal posterior densities and calculates posterior probabilities of nonstationarity.
ARMA Model Selection and Estimation - Estimate ARMA models by recursive techniques that include automated order selection procedures. Choose a model selection method like AIC, BIC or PIC, find a suitable model for your data and use graphical displays with built-in unit root tests in your evaluation.
Kernel Estimation - Access a full library of kernel estimation routines for the estimation of spectra, long run variances, and one-sided long run covariances. Data-driven bandwidth methods are available as well as the latest AR- and ARMA-prefiltered kernel procedures. COINT is supplied with a complete reference manual for the use of all of its procedures, a bibliography to the literature, and full instructions for set-up and installation with GAUSS. COINT is supplied in GAUSS source code so that, as a user, you have access to the code for your own personal use in teaching and research.

Available for Windows, LINUX, UNIX, requires GAUSS version 3.2 and above.

via Rettangolo, 12/14 - 67039 - Sulmona (AQ) - Italia