BprobitEx: menus and dialogs

Menus
Dialogs for model formulation and estimation:: Formulate - Binary Discrete Choice; Formulate - Count Data; Model Settings - Binary Discrete Choice; Model Settings - Count Data; Estimate; Options; Progress
Dialogs for model evaluation:: Further Output; Graphic Analysis; Norm Observation; Outliers; Predictions; Exclusion Restrictions; Linear Restrictions; Store in Database; Test

Formulate Model - Binary Discrete Choice

Use this dialog for to formulate a new model, or reformulate an existing model.

Database

Mark all the variables you wish to include in the new model or add to the existing model, using the spacebar or the mouse. After you have pressed << (or double-clicked if you are using a mouse), the database variables are added to the model.

The variable at the top of the list will by default become the endogenous (Y) variable. To select a different dependent variable, see below.

Special

The listbox below the database variables shows the so-called special variables, which are pre-defined. Here it is:

Constant
A constant will be added automatically in a new model but can be deleted.

Selection

This list box shows the current model.
The variable at the top of the list will by default become the endogenous (Y) variable.
To select a dependent variable which is listed further down:

mark the current dependent variable and right-click to clear its status;
mark the new variable, and right-click to change to Y: endogenous.

If you have marked variables in the model, you can delete them, or assign a status to them.

<<

Adds the currently selected database or special variables to the model.

>>

Deletes the currently selected variables from the model. You can also double click on a model variable to delete it.

Clear>>

Deletes the whole model, so that you can start from scratch.

Status

The status drop-down box lists all the available variable types for the current model class. If model variables are selected, a new status can be chosen, and set using the Set button.

Clear status Clears the status of all selected model variables. Cleared variables behave as X variables.
Y: endogenous Label the current model selection as endogenous variables.
Normally, there is one dependent variable which holds the binary response 0 or 1 (although 1 and 2 are also allowed). If two Y variables are marked, the data are assumed to be grouped.
X: variable Marks the selected model variables as a normal regressor. This is the default for an unmarked variable, so an X variable or unmarked variable are treated in the same way.
S: Select By By default, all valid observations are used for estimation. Select by can be used to estimate over a sub-sample. When a model variable is marked as S variable, only observations which have a non-zero value are included for estimation. When predicting, the default is to use all valid observations which were not used in estimation, but it is also possible to only predict for observations which have a value 2 for the select by variable.

Previous models

Use this to recall a previously estimated model.

Formulate - Count Data

Use this dialog for to formulate a new model, or reformulate an existing model.

Database

The variable at the top of the list will by default become the endogenous (Y) variable. To select a different dependent variable, see below.

Special

The listbox below the database variables shows the so-called special variables, which are pre-defined. Here it is:

Constant
A constant will be added automatically in a new model but can be deleted.

Selection

This list box shows the current model.
The variable at the top of the list will by default become the endogenous (Y) variable.
To select a dependent variable which is listed further down:

mark the current dependent variable and right-click to clear its status;
mark the new variable, and right-click to change to Y: endogenous.

If you have marked variables in the model, you can delete them, or assign a status to them.

<<

Adds the currently selected database or special variables to the model.

>>

Deletes the currently selected variables from the model. You can also double click on a model variable to delete it.

Clear>>

Deletes the whole model, so that you can start from scratch.

Status

The status drop-down box lists all the available variable types for the current model class. If model variables are selected, a new status can be chosen, and set using the Set button.

Clear status Clears the status of all selected model variables. Cleared variables behave as X variables.
Y:endogenous Label the current model selection as the endogenous variable. Only one endogenous variable, holding the counts, is allowed.
X:variable Marks the selected model variables as a normal regressor. This is the default for an unmarked variable, so an X variable or unmarked variable are treated in the same way.
S: Select By By default, all valid observations are used for estimation. Select by can be used to estimate over a sub-sample. When a model variable is marked as S variable, only observations which have a non-zero value are included for estimation.

Previous models

Use this to recall a previously estimated model.

Model Settings - Binary Discrete Choice

This dialog is for choosing a discrete choice model.

The Model Type

Logit
Probit

Only binary probit can be estimated.

Model Settings - Count Data

This dialog is for choosing a count data model.

The Model Type

Poisson
Negative binomial

Estimate

Select an estimation method for the formulated model.

Estimation sample

Cross-section modelling automatically drops all observations with missing values. This can be refined by specifying a select by variable in the model formulation stage.

Estimation method

Newton's method

OK

Pressing OK starts the estimation, unless there still is something missing or wrong in the dialog.

Options

Controls maximization settings, and what is automatically printed after estimation (in addition to the normal estimation report). Model options referes to settings which are changed infrequently, and are persistent between runs of OxPack.

Maximization Settings

Maximum number of iterations:
Note that it is possible that the maximum number of iterations is reached before convergence. The maximum number of iterations also equals the maximum number of switches in cointegration.

Write results every:
By default no iteration progress is displayed in the results window. It is possible to write intermediate information to the Results window for a more permanent record. A zero (the default) will write nothing, a 1 every iteration, a 2 every other iteration, etc.

Write in compact form:
Writes one line per printed iteration (see Write results every).

Convergence tolerance:
Change the convergence tolerance levels (the smaller, the longer the estimation will take to converge).

Default:
Resets the default maximization settings.

Progress

The Progress dialog is used to review the progress made to date in the model reduction.

To offer a default sequence, OxPack decides that model A could be nested in model B if the following conditions hold:

model A must have a lower log-likelihood (i.e.~higher RSS),
model A must have fewer parameters,
model A and B must have the same sample period and database.

PcGive does not check if the same variables are involved, because transformations could hide this. As a consequence PcGive does not always get the correct nesting sequence, and it is the user's responsability to ensure nesting.

There are two options on the dialog to select a nesting sequence:

Mark Specific to General: Marks more general models, finding a nesting sequence with strictly increasing log-likelihood.
Mark General to Specific: Marks all specific models that have a lower log-likelihood.

The default selection is found by first setting the most recent model as specific, and then setting the general model that was found as the general model.

Additional dialog items are:

<: To move a model up in the modelling sequence.
>: To move a model down in the modelling sequence.
Del: Tp permanently delete a model from the modelling sequence.
OK: Prints the progress report, consisting of:
1. number of observations, paramaters, and log-likelihood.
2. Information criteria: reported are the Schwarz Criterion (SC), the Hannan-Quinn (HQ) Criterion, and the Akaike criterion (AIC).
3. F or Chi-squared tests of each reduction.

Graphic Analysis

Predictions

Further Output

Outliers

Norm Observation

Exclusion Restrictions

Allows you to select explanatory variables and test whether they are jointly significant. A more general form is the test for linear restrictions.

Selection: Mark all the variables you wish to include in the test in this list box.

OxPack tests whether the selected variables can be deleted from the model.

Linear Restrictions

Tests for linear restrictions are specified in the form of a matrix R, and a vector r. These are entered as one matrix [R : r]' in the dialog. (This is a more general than testing for exclusion restrictions.)

For example, if the model is CONS on Constant, CONS_1, INC, INC_1, and we wish to test that the coefficients on INC and INC_1 add up to one, and that on CONS_1 equals zero. Then the R:r matrix can be written as

The first four rows are the columns of R, specifying two restrictions. The last row is r, which specifies what the restrictions should add up to.

The dimensions of the matrix must be specified in the rows and columns fields.

Matrix: This window is a matrix editor in which you can specify the values, very similar to an OxMetrics database.
Rows: The number of rows in the matrix.
Columns: The number of columns in the matrix.
Load: Enables you to load an existing matrix file into the editor. Any existing matrix in the editor will be lost.
Save: Enables you to save the contents of the editor in an matrix file, so that it can be used again.

Store in database dialog

Allows you to save any of the listed items in the OxMetrics database.

This file last changed .