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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.
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:
yt = Czt + et (observation equation)
zt+1 = Az t + Bet (state equation)
where yt is an (m x 1) vector of the time series to be modeled and/or forecast, zt is the (n x 1) state vector, et 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 LÜTKEPOHL [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 (Lütkepohl 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.
© Copyright 2010 Aptech Systems, Inc.


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