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Third Party Applications for GAUSS
Related software programs developed by
third-party vendors. Many of these products are
pre-written, customizable programs written in
the GAUSS Programming Language, as well as tools
and utilities to get the job done, and get it
done FAST!
Further details on these products can be
obtained via email from TStat S.r.l.
Stat/Transfer
13: 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 13 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
- and more..
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
- Intel/AMD (x86) (32-Bit Linux)
- Intel/AMD (x86-64) (64-Bit Linux)
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
10: Full set of professional econometrics
routines
Gaussx 10 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.
Gaussx 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 Gaussx.
New Features in Gaussx 10
GAUSS
11 Support
Gaussx has been updated to
support the new GAUSS 11 interface, and
incorporate the new functionality of GAUSS 11.
Copulas
A copula is used in statistics as a general way
of formulating a multivariate distribution with
a specified correlation structure:
Example:
let rmat[3,3] =
1 .5 .2 .5 1 .6 .2 .2 .6 1;
q = copula(1000,rmat,1);
v1 = normal_cdfi(q[.,1], 0, 1);
v2 = expon_cdfi(q[.,2], 2);
v3 = gamma_cdfi(q[.,3], 1.5, 2.5);
q is a 1000x3 copula matrix with a Kendal Tau
correlation structure given by rmat. This copula
is then used to create three correlated random
deviates drawn from the normal, exponential and
gamma distributions.
CORR
Computes a correlation matrix for different
correlation types - Pearson, Kendall Tau b and
Spearman Rank.
MVRND
Creates a matrix of (pseudo) correlated random
variables using specified distributions.
Example:
dist = "normal"
$| "expon" $| "gamma";
let p[3,3] = 0 1 0 0 1 0 0 0 1.5 2.5 0 0;
let rmat[3,4] = 1 .5 .2 .5 1 .6 .2 .6 1;
s = mvrnd(1000, 3, dist, p, rmat, 2);
This example creates s, which is a 1000x3 matrix
of correlated random variates consisting of the
three distributions shown in dist, with the
correlation structure specified by the Spearman
rank matrix rmat.
STEPWISE
In a situation where there are a large number of
potential explanatory variables, STEPWISE can be
useful in ascertaining which combination of
variables are significant, based on the F
statistic. It includes the capability of scaling
data, and expanding a given data set to include
cross and/or quad terms. This is an exploratory,
rather than a rigorous tool.
Example:
oplist = { .4
.25 };
indx = stepwise(y~xmat, 0, oplist);
{xnew, xname} = xmat[.,indx];
This example shows how a stepwise regression is
applied to a matrix of potential explanatory
variables xmat, using .4 and .25 for the F
statistic probability of entry and exit.
Latin Hypercube
Sample - LHS
LHS has the advantage of generating a set of
samples that more precisely reflect the shape of
a sampled distribution than pure random (Monte
Carlo) samples. The Gaussx implementation
provides standard LHS, nearly orthogonal LHS,
and correlation LHS.
Example
n = 30; k = 6;
fill = 0; ntry = 1000; crit = 2;
dsgn = fill | ntry | crit;
p = lhs(n,k,dsgn);
x = weibull_cdfi(p,1,1.5);
In this example, a 30x6 nearly orthogonal Latin
Hypercube Sample is derived using the best
condition number as the criteria. This creates a
30x6 matrix of probabilities, which are then
used to create a set of Weibull distributed
variates, each column being orthogonal to every
other column.
STATLIB -
Statistical Distribution Library
The STATLIB library has been updated; it now
includes 51 continuous distributions, and 9
discrete distributions. This library can be used
independently of Gaussx, or as part of Gaussx -
for example in an ML context.
In the context of ML estimation, the parameters
of a particular distribution can be estimated
from a set of data, or a parameter can be
replaced by a linear or non-linear function,
whose parameter can also be estimated. Threshold
estimates for distributions where the data is
non-negative is also supported.
Example:
x =
seqa(0,.2,6);
a = 2; b = 4;
p = beta_pdf(x,a,b);
param b0 b1;
value = .1 1 ;
FRML eq1 v = b0 + b1*x;
FRML eq2 llfn = chisq_llf(y,v);
ML (d,p,i) eq1 eq2
method = nr nr nr;
The first example shows pure GAUSS code for
estimating the pdf for a beta distribution. The
second shows how the parameters of a function
which is used to replace a parameter in a
distribution can be evaluated.
Platforms:
Windows, Mac OS X, LINUX, UNIX (requires GAUSS
for Windows 6.0 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
Platforms:
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 8.0: 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 v8.0
Mercury_GAUSS consists of a set of
functions that provide an interface between an
external application and GAUSS. These
functions permit sending strings, values and
data from the external application to GAUSS,
running GAUSS code or procedures, and returning
the data back to the external application.
Mercury has four main components:
- Excel Link – an application that links
GAUSS to an Excel worksheet.
- An Excel addin that links an Excel
Workbook to GAUSS. Using VBA, data is
sent to GAUSS where it is processed, and the
results are returned to the specified cells
in the spreadsheet.
- An Excel function – geFN – that permits
any GAUSS function to be called directly
from an Excel cell.
- A library of interface functions for
developers who need to link GAUSS to an
external application using custom
interfaces.
- Windows clipboard support for GAUSS.
- A demonstration project showing how Gauss
compliant DLLs are created using Visual
Studio
Extend the functionality of GAUSS in your
other applications. For example:
- You can design a custom front end using
Visual Studio,
- Extend the functionality of Excel to
include all Gauss commands.
- Provide an easy way of copying data into a
GAUSS variable using the clipboard.
- Write your own GAUSS libraries to extend
the functionality of GAUSS.
- Use Excel as a data entry application for
GAUSS.
Sample demonstration projects are included for:
- Excel
- VC6, MFC, C++, and C#
- VB6 and VB.NET
New and recent Features:
- Excel link between GAUSS and Excel
- GUI functions for GAUSS
- Data interface for GAUSS using DataGrid
- Multi-application support
- Thread safe execution
- geFN Excel function
- Mercury classes
- 32 and 64 bit support
Requirements:
- Mercury_GAUSS requires GAUSS for Windows 6.0 or higher, and supports both 32 and 64 bit versions of GAUSS.
Platforms:
- Windows XP, Vista, Win7 – x86 or x64.
Mercury
GE 8.1: 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
v8.1
- 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 2.0 :
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.
Requirements:
Operating System: Windows
GAUSS: GAUSS Mathematical & Statistical System v4.0+ for Windows or GAUSS Engine v4.0+ for Windows
Maple: 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.
Platform:
Windows, LINUX, UNIX
Requires:
GAUSS Mathematical & Statistical System v3.2 and above.
GENO 2.0:
General Evolutionary Numerical Optimizer GENO
is an acronym that stands for General Evolutionary Numerical Optimizer.
GENO is a real-coded genetic algorithm for solving single or
multi-objective optimization problems that may be static or dynamic in
character; unconstrained or constrained by functional equalities or
inequalities, as well as by upper and lower bounds on the variables;
the choice variables themselves may assume real or discrete values in
any combination. In short, the algorithm does not require the problem
to have any special structure.
GENO has proven its worth in the market place: the client base includes
individual researchers, various university departments and research
institutes, major central banks, an oil company, an insurance company
and a major car manufacturer; GENO computes solutions that are regarded
as benchmarks for other algorithm designs to emulate.
GENO 2.0 Features:
- Internal genetic operators have been re-designed resulting in a vast improvement in performance
- Provision for solving nonlinear systems of equations, as well as goal programming models have been added
- Program is much easier to use because some data and parameter requirements have been automated and internalized
- User may now specify that a particular point in the search space should be included in the solution process
Scope of Application:
- GENO may be specialized in situ to solve various classes of problems by mere choice of a few parameters. It applies to:
- - systems of linear or nonlinear equations
- - static or dynamic optimization problems, with or without functional and/or set-constraints
- - single, as well as multi-objective problems
- It may be set to generate real or integer-valued solutions, or a mixture of the two as required.
- The scope of its application includes:
- - Static Optimization
- - Dynamic Optimization
- - Robust Optimization
- - Mixed Variable Optimization
- - Multi-objective Optimization
- - Nonlinear Equation Systems
- - Goal Programming
Product Attributes:
- Documention:
GENO is well documented and easy to use; the product includes a large
number of examples programs to help kick-start user experience.
- Testing: The
algorithm has been tested on a wide range of real-life and artificial
problems from well-known test suites; it has also been tested against
well known algorithms that are embedded in popular computational
systems including Mathematica, Global Optimization and MathOptimizer;
it consistently out-performs many evolutionary algorithms, and the
quality of its final solution is at least as good as several specialist
deterministic algorithms in many cases
- Practical Example Problems: A partial list included in the product is as follows:
- - The Economic Dispatch Problem
- - The Alkylation Process
- - Decentralized Economic Planning
- - Heat Exchanger Optimization
- - Asset Portfolio Optimization
- - Job Shop Scheduling
- - Market Equilibrium Problem
- - Chemical Process Synthesis
- - General Resource Allocation
Operating Systems: Windows, Linux, Mac OS X
Requires: GAUSS 10 or later
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.
Platforms: 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.
Platforms: Windows, LINUX, UNIX
Requirements: GAUSS version 3.2 and above.
© Copyright 2015 Aptech
Systems, Inc.
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