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ALOGIT has been in intensive use by leading-edge 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 log-normal 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;

-      non-linear 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, well-laid-out 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.