# BprobitEx: menus and dialogs

## Contents

Menus
  File menu
  Package menu
  Model menu
  Test menu
  Help menu
Model Menu Dialogs
  Estimate Model dialog box
  Formulate Model dialog box (binary discrete choice)
  Formulate Model dialog box (count data)
  Model Settings dialog box (discrete choice)
  Model Settings dialog box (count data)
  Options dialog box
  Progress dialog box
  Recall dialog box
Test Menu Dialogs
  Exclusion Restrictions dialog box
  Further output dialog box
  Graphic analysis dialog box
  Linear Restrictions dialog box
  Norm observation dialog box
  Outliers dialog box
  Predictions dialog box
  Store in database dialog
  Test dialog box
Help
  Help

### File Menu

Always on Top
  This menu item toggles the always on top status. When switched on, OxPack will always be above other windows, even if it does not have the focus. When OxPack is shrunk to a small window, this could be useful to allow easy access. When off, the OxPack will behave as a normal window.

  Note that the setting is persistent between runs.

Exit
  Exits the application.

### Package Menu

Select BprobitEx from the Package menu

### Model Class Menu

Select one of the following model classes:

- 1: Binary Discrete Choice
- 2: Count Data

### Model Menu

The Model menu allows you to formulate and estimate models of the selected model class, recall a previously estimated model, check the progress made in the modelling process.

The following commands are available:
Formulate
Model Settings
Estimate
Model Options
Progress

### Test Menu

The Test menu is used to evaluate the model graphically, and through diagnostic testing.

- Graphic analysis
- Predictions
- Further Output
- Norm Observation
- Outliers
- Exclusion restrictions
- Linear restrictions
- Store in database

### Help Menu

Press the F1 key for help. Or click on the Help menu and select Help Topics to see the help index.

Press F1 or click on the Help button in any dialog for context-specific help.

### Formulate Model dialog box (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, in this Multiple-Selection List box, using the spacebar or the mouse.

  After you have pressed Enter (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
  - `Constant`
    A constant will be added automatically in a new model but can be deleted.

Model
  This Multiple-Selection 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:
  1. mark the current dependent variable and clear its status;
  2. mark the new variable, and press the Y:Endogenous button.
  If you have marked variables in the model, you can delete them, or assign a status to them.
Delete
  Deletes the current model selection.
New model
  Deletes the whole model, so that you can start from scratch.

Clear
  Clears the status of all selected model variables. Cleared variables behave as X variables. You can also double click on a model variable to clear its status.
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.
W: weight
  Optionally, a variable can be marked for weighted maximum likelihood estimation.
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.

OK or Add
  If there are still database variables marked, this button will be called Add. Press it to add the variables (or press Deselect All to change it to OK). You will be prompted for a lag length if it is set to query. You can also double click on a database variable to add it to the model.
  Press OK to move to the Model Settings or Estimation.
Cancel
  Equivalent to pressing Esc, it will abort the model formulation.

### Formulate Model dialog box (Count data)

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, in this Multiple-Selection List box, using the spacebar or the mouse.

  After you have pressed Enter (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
  - `Constant`
    A constant will be added automatically in a new model but can be deleted.

Model
  This Multiple-Selection 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:
  1. mark the current dependent variable and clear its status;
  2. mark the new variable, and press the Y:Endogenous button.
  If you have marked variables in the model, you can delete them, or assign a status to them.
Delete
  Deletes the current model selection.
New model
  Deletes the whole model, so that you can start from scratch.

Clear
  Clears the status of all selected model variables. Cleared variables behave as X variables. You can also double click on a model variable to clear its status.
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.

OK or Add
  If there are still database variables marked, this button will be called Add. Press it to add the variables (or press Deselect All to change it to OK). You will be prompted for a lag length if it is set to query. You can also double click on a database variable to add it to the model.
  Press OK to move to the Model Settings or Estimation.
Cancel
  Equivalent to pressing Esc, it will abort the model formulation.

### Recall dialog box

Use this dialog to recall a previously estimated model.
You will have to re-estimate it to get access to the items on the Test Menu.

Models
  Move the cursor to the model you wish to recall, and press OK.
Previous, Next
  Use these buttons to move between the estimated models.

### Model Settings dialog box (discrete choice)

This dialog is for choosing a discrete choice model.

The Model Type
  - Logit
  - Probit
    Only binary probit can be estimated.

### Model Settings dialog box (count data)

This dialog is for choosing a count data model.

The Model Type
  - Poisson
  - Negative binomial

### Estimate Model dialog box

Select an estimation method for the formulated model.

The Method Options
  - Newton's method

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

### Model Options dialog box

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). See under numerical optimization for an explanation of convergence decisions.

  Default:
  Resets the default maximization settings.

### Progress dialog box

Use this dialog to review the progress made to date in the model reduction, when using the general to specific Modelling Strategy.

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.

OxPack does not check if the same variables are involved, because transformations could hide this. As a consequence OxPack 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:

Set Specific
  Marks more general models, finding a nesting sequence with strictly increasing log-likelihood.
Set General
  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:

Models
  Already marked is the default nesting sequence. However, OxPack might miss a model that could be nested through transformed variables. You can add such models to the nesting chain by marking them in this list box. Marking or unmarking can be done by clicking on a model's checkbox.

  Models can be moved up or down by selecting them and pressing the arrow up or down button.

Find Results
  Will exit the dialog and try to locate the output of the highlighted model in the GiveWin results window.
Write Batch
  Writes the batch code for the selected models.
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 dialog box

### Predictions dialog box

### Further output dialog box

### Outliers dialog box

### Norm observation dialog box

### Exclusion Restrictions dialog box

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 Multiple-Selection List box.

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

### Linear Restrictions dialog box

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

        0 1 0 0  0
        0 0 1 1  1

The first four columns are the columns of R, specifying two restrictions. The last column 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. It is your responsibility to specify the right values, OxPack will not try to work it out (because elements of a row may be spread over several lines).

Rows
  The number of rows in the matrix.
Columns
  The number of columns in the matrix.
Matrix
  This window is a basic text editor in which you can edit a matrix file. Here you can enter the R:r matrix as in the above example.
Set to zero
  This could be useful to create an initial matrix. Select variables in the model box (this is a this multiple-selection list box). and press this button to specify the R:r matrix which corresponds to the restriction that each selected variable has coefficient zero (so one row for each selected variable)
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

### Multiple-Selection List boxes

A multiple selection list box allows for marking as many items as desired.

With the keyboard it is only possible to mark a single variable (by using the arrow up and down keys), or range of variables (hold the Shift key down while using the arrow up or down keys).

With the mouse there is more flexibility:

- single click to select one variable;
- hold the left mouse button down to select a range of variables;
- hold the Ctrl key down and click to select additional variables;
- hold the Shift key down and click to extend the selection range.

### Modelling strategy

OxPack has two modes of operation: general-to-specific and unordered.

General-to-specific
  1. Begin with the model formulation;
  2. Check its data coherence and cointegration;
  3. Transform to a set of variables with low intercorrelations but interpretable parameters;
  4. Delete unwanted regressors to obtain a parsimonious model;
  5. Check the validity of the model by thorough testing and graphical inspection.

OxPack monitors the progress of the sequential reduction from the general to the specific and will provide the associated F-tests or likelihood-ratio tests, and information criteria.

Unordered Search
  Nothing commends unordered searches:
  1. No control is offered over the significance level of testing;
  2. A `later' reject outcome invalidates all earlier ones;
  3. Until a model adequately characterizes the data, standard tests are invalid

This file last changed
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