Capabilities 


ALOGIT has been in intensive use by leadingedge
modellers for more than 30 years and has been developed throughout that
period to meet the needs of advanced modelling. As a result, ALOGIT has a high level of
reliability and numerous features and facilities that are useful for
professional modelling. The Help file
gives an overview of the possibilities (download zipped
version of Help but note that this does not work with the most recent
versions of Windows). ALOGIT estimates the parameters of generalised logit
models. The main generalisations are 
tree
(nested, hierarchical) models allowing the alternatives in the model to be
related in less retrained ways than in simple logit models but still
retaining ease of use and speed of operation; 
mixed logit
models,
implemented using the flexible ‘error components’ specification, which works
with either linear or the
exponentiated form, which allows, for instance lognormal disturbances in the
coefficients. Mixed logit models are possible only with
the EC variant of the software. ALOGIT performs four key functions. Data input:  revealed preference or stated preference, disaggregate or
aggregate data can be used; choice, ranking or proportional split data
can be employed;  input data can be manipulated and transformed freely
to allow the user freedom in finding the required explanation of behaviour;
extensive testing of input data to reveal modelling problems; simple
controls, yet giving access when required to full sophistication;  multiple data sets of different formats are accepted, either in succession or linked
using named (key) variables; some binary matrix formats are supported. Model estimation:  all coefficients are estimated simultaneously
using maximum likelihood estimation (i.e. ‘full information’ estimates);  models can be binomial, multinomial, or tree (nested) logit models, with
unlimited branches and levels; alternatively, mixed logit or error
component analysis, including differing distributions (including
exponentials, e.g. lognormal) and correlated error terms;  nonlinear utility functions allow attraction
variables to be included correctly;  composite alternatives can be indicated as chosen;  coefficient estimates, standard errors, correctly
calculated elasticities, consumer surplus measures and several detailed tests
are all standard, with informative, clearly labelled, welllaidout output,
suitable for immediate incorporation in reports;  a function in the ALOGIT Shell
can be used to make comparisons between different model variants;  an option for initial linear estimation reduces run
time for complex models;  problem
sizes are not limited by ALOGIT. Forecasting:  the user can specify detailed scenarios which
incorporate a series of changes in the variables influencing choice, and
ALOGIT can predict, display and analyse the consequent changes in behaviour;  a function in the ALOGIT Shell
can be used to make graphical and tabular presentations of scenario outputs;
alternatively, output can be made to other programs such as Excel. Data processing:  ALOGIT can be used for a range of simple data
processing tasks, using the control language and statistical reporting
procedures to give an efficient working environment;  in particular, files can be output in very flexible
ways (e.g. reformatted, respecified, sorted) for
processing by other software. All of these functions are controlled by ALOGIT’s intuitive control file, with  a very flexible command language, including named variables, intuitively appealing
definition structure for hierarchical models, named Boolean operators (TRUE,
FALSE, AVAIL etc.);  include file option
for use of external files with command lines or coefficients, streamlining
model (estimation) management;  array definition of alternatives, system data items and variables;  random number generator (using uniform, normal, logistic distributions or
assignment of multinomial variable with specific probabilities);  ‘if ... THEN
... ELSE... END’ and ‘DO ... END’ syntax, to simplify data
transformations;  intuitive specification of tree logit models using $NEST commands. 